diff --git a/public/portal.html b/public/portal.html
index 03b01f1..bc2c068 100644
--- a/public/portal.html
+++ b/public/portal.html
@@ -67,155 +67,174 @@
<button id="model-gemini-ultra" data-value="gemini-1.0-ultra">Gemini Ultra</button>
<!-- Add other models as options -->
<!-- Text Models: Show Tokens per $ -->
+ <button id="open-router-model-neversleep-llama-3.1-lumimaid-70b" data-value="neversleep/llama-3.1-lumimaid-70b">Lumimaid v0.2 70B</button>
+ <button id="open-router-model-anthracite-org-magnum-v4-72b" data-value="anthracite-org/magnum-v4-72b">Magnum v4 72B</button>
+ <button id="open-router-model-anthropic-claude-3.5-sonnet" data-value="anthropic/claude-3.5-sonnet">Anthropic: Claude 3.5 Sonnet</button>
+ <button id="open-router-model-anthropic-claude-3.5-sonnet-beta" data-value="anthropic/claude-3.5-sonnet:beta">Anthropic: Claude 3.5 Sonnet (self-moderated)</button>
+ <button id="open-router-model-x-ai-grok-beta" data-value="x-ai/grok-beta">xAI: Grok Beta</button>
+ <button id="open-router-model-mistralai-ministral-8b" data-value="mistralai/ministral-8b">Ministral 8B</button>
+ <button id="open-router-model-mistralai-ministral-3b" data-value="mistralai/ministral-3b">Ministral 3B</button>
+ <button id="open-router-model-qwen-qwen-2.5-7b-instruct" data-value="qwen/qwen-2.5-7b-instruct">Qwen2.5 7B Instruct</button>
+ <button id="open-router-model-nvidia-llama-3.1-nemotron-70b-instruct" data-value="nvidia/llama-3.1-nemotron-70b-instruct">NVIDIA: Llama 3.1 Nemotron 70B Instruct</button>
+ <button id="open-router-model-inflection-inflection-3-pi" data-value="inflection/inflection-3-pi">Inflection: Inflection 3 Pi</button>
+ <button id="open-router-model-inflection-inflection-3-productivity" data-value="inflection/inflection-3-productivity">Inflection: Inflection 3 Productivity</button>
+ <button id="open-router-model-google-gemini-flash-1.5-8b" data-value="google/gemini-flash-1.5-8b">Google: Gemini 1.5 Flash-8B</button>
+ <button id="open-router-model-liquid-lfm-40b-free" data-value="liquid/lfm-40b:free">Liquid: LFM 40B MoE (free)</button>
+ <button id="open-router-model-liquid-lfm-40b" data-value="liquid/lfm-40b">Liquid: LFM 40B MoE</button>
+ <button id="open-router-model-thedrummer-rocinante-12b" data-value="thedrummer/rocinante-12b">Rocinante 12B</button>
+ <button id="open-router-model-eva-unit-01-eva-qwen-2.5-14b" data-value="eva-unit-01/eva-qwen-2.5-14b">EVA Qwen2.5 14B</button>
+ <button id="open-router-model-anthracite-org-magnum-v2-72b" data-value="anthracite-org/magnum-v2-72b">Magnum v2 72B</button>
+ <button id="open-router-model-meta-llama-llama-3.2-3b-instruct-free" data-value="meta-llama/llama-3.2-3b-instruct:free">Meta: Llama 3.2 3B Instruct (free)</button>
+ <button id="open-router-model-meta-llama-llama-3.2-3b-instruct" data-value="meta-llama/llama-3.2-3b-instruct">Meta: Llama 3.2 3B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3.2-1b-instruct-free" data-value="meta-llama/llama-3.2-1b-instruct:free">Meta: Llama 3.2 1B Instruct (free)</button>
+ <button id="open-router-model-meta-llama-llama-3.2-1b-instruct" data-value="meta-llama/llama-3.2-1b-instruct">Meta: Llama 3.2 1B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3.2-90b-vision-instruct" data-value="meta-llama/llama-3.2-90b-vision-instruct">Meta: Llama 3.2 90B Vision Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3.2-11b-vision-instruct-free" data-value="meta-llama/llama-3.2-11b-vision-instruct:free">Meta: Llama 3.2 11B Vision Instruct (free)</button>
+ <button id="open-router-model-meta-llama-llama-3.2-11b-vision-instruct" data-value="meta-llama/llama-3.2-11b-vision-instruct">Meta: Llama 3.2 11B Vision Instruct</button>
+ <button id="open-router-model-qwen-qwen-2.5-72b-instruct" data-value="qwen/qwen-2.5-72b-instruct">Qwen2.5 72B Instruct</button>
+ <button id="open-router-model-qwen-qwen-2-vl-72b-instruct" data-value="qwen/qwen-2-vl-72b-instruct">Qwen2-VL 72B Instruct</button>
+ <button id="open-router-model-neversleep-llama-3.1-lumimaid-8b" data-value="neversleep/llama-3.1-lumimaid-8b">Lumimaid v0.2 8B</button>
+ <button id="open-router-model-openai-o1-mini-2024-09-12" data-value="openai/o1-mini-2024-09-12">OpenAI: o1-mini (2024-09-12)</button>
+ <button id="open-router-model-openai-o1-mini" data-value="openai/o1-mini">OpenAI: o1-mini</button>
+ <button id="open-router-model-openai-o1-preview-2024-09-12" data-value="openai/o1-preview-2024-09-12">OpenAI: o1-preview (2024-09-12)</button>
+ <button id="open-router-model-openai-o1-preview" data-value="openai/o1-preview">OpenAI: o1-preview</button>
+ <button id="open-router-model-mistralai-pixtral-12b" data-value="mistralai/pixtral-12b">Mistral: Pixtral 12B</button>
+ <button id="open-router-model-cohere-command-r-plus-08-2024" data-value="cohere/command-r-plus-08-2024">Cohere: Command R+ (08-2024)</button>
+ <button id="open-router-model-cohere-command-r-08-2024" data-value="cohere/command-r-08-2024">Cohere: Command R (08-2024)</button>
+ <button id="open-router-model-qwen-qwen-2-vl-7b-instruct" data-value="qwen/qwen-2-vl-7b-instruct">Qwen2-VL 7B Instruct</button>
+ <button id="open-router-model-google-gemini-flash-1.5-8b-exp" data-value="google/gemini-flash-1.5-8b-exp">Google: Gemini Flash 8B 1.5 Experimental</button>
+ <button id="open-router-model-sao10k-l3.1-euryale-70b" data-value="sao10k/l3.1-euryale-70b">Llama 3.1 Euryale 70B v2.2</button>
+ <button id="open-router-model-google-gemini-flash-1.5-exp" data-value="google/gemini-flash-1.5-exp">Google: Gemini Flash 1.5 Experimental</button>
+ <button id="open-router-model-ai21-jamba-1-5-large" data-value="ai21/jamba-1-5-large">AI21: Jamba 1.5 Large</button>
+ <button id="open-router-model-ai21-jamba-1-5-mini" data-value="ai21/jamba-1-5-mini">AI21: Jamba 1.5 Mini</button>
+ <button id="open-router-model-microsoft-phi-3.5-mini-128k-instruct" data-value="microsoft/phi-3.5-mini-128k-instruct">Phi-3.5 Mini 128K Instruct</button>
+ <button id="open-router-model-nousresearch-hermes-3-llama-3.1-70b" data-value="nousresearch/hermes-3-llama-3.1-70b">Nous: Hermes 3 70B Instruct</button>
+ <button id="open-router-model-nousresearch-hermes-3-llama-3.1-405b-free" data-value="nousresearch/hermes-3-llama-3.1-405b:free">Nous: Hermes 3 405B Instruct (free)</button>
+ <button id="open-router-model-nousresearch-hermes-3-llama-3.1-405b" data-value="nousresearch/hermes-3-llama-3.1-405b">Nous: Hermes 3 405B Instruct</button>
+ <button id="open-router-model-nousresearch-hermes-3-llama-3.1-405b-extended" data-value="nousresearch/hermes-3-llama-3.1-405b:extended">Nous: Hermes 3 405B Instruct (extended)</button>
+ <button id="open-router-model-perplexity-llama-3.1-sonar-huge-128k-online" data-value="perplexity/llama-3.1-sonar-huge-128k-online">Perplexity: Llama 3.1 Sonar 405B Online</button>
+ <button id="open-router-model-openai-chatgpt-4o-latest" data-value="openai/chatgpt-4o-latest">OpenAI: ChatGPT-4o</button>
+ <button id="open-router-model-sao10k-l3-lunaris-8b" data-value="sao10k/l3-lunaris-8b">Llama 3 8B Lunaris</button>
+ <button id="open-router-model-aetherwiing-mn-starcannon-12b" data-value="aetherwiing/mn-starcannon-12b">Mistral Nemo 12B Starcannon</button>
+ <button id="open-router-model-openai-gpt-4o-2024-08-06" data-value="openai/gpt-4o-2024-08-06">OpenAI: GPT-4o (2024-08-06)</button>
+ <button id="open-router-model-meta-llama-llama-3.1-405b" data-value="meta-llama/llama-3.1-405b">Meta: Llama 3.1 405B (base)</button>
+ <button id="open-router-model-nothingiisreal-mn-celeste-12b" data-value="nothingiisreal/mn-celeste-12b">Mistral Nemo 12B Celeste</button>
+ <button id="open-router-model-google-gemini-pro-1.5-exp" data-value="google/gemini-pro-1.5-exp">Google: Gemini Pro 1.5 Experimental</button>
+ <button id="open-router-model-perplexity-llama-3.1-sonar-large-128k-online" data-value="perplexity/llama-3.1-sonar-large-128k-online">Perplexity: Llama 3.1 Sonar 70B Online</button>
+ <button id="open-router-model-perplexity-llama-3.1-sonar-large-128k-chat" data-value="perplexity/llama-3.1-sonar-large-128k-chat">Perplexity: Llama 3.1 Sonar 70B</button>
+ <button id="open-router-model-perplexity-llama-3.1-sonar-small-128k-online" data-value="perplexity/llama-3.1-sonar-small-128k-online">Perplexity: Llama 3.1 Sonar 8B Online</button>
+ <button id="open-router-model-perplexity-llama-3.1-sonar-small-128k-chat" data-value="perplexity/llama-3.1-sonar-small-128k-chat">Perplexity: Llama 3.1 Sonar 8B</button>
+ <button id="open-router-model-meta-llama-llama-3.1-70b-instruct-free" data-value="meta-llama/llama-3.1-70b-instruct:free">Meta: Llama 3.1 70B Instruct (free)</button>
<button id="open-router-model-meta-llama-llama-3.1-70b-instruct" data-value="meta-llama/llama-3.1-70b-instruct">Meta: Llama 3.1 70B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3.1-70b-instruct-nitro" data-value="meta-llama/llama-3.1-70b-instruct:nitro">Meta: Llama 3.1 70B Instruct (nitro)</button>
+ <button id="open-router-model-meta-llama-llama-3.1-8b-instruct-free" data-value="meta-llama/llama-3.1-8b-instruct:free">Meta: Llama 3.1 8B Instruct (free)</button>
<button id="open-router-model-meta-llama-llama-3.1-8b-instruct" data-value="meta-llama/llama-3.1-8b-instruct">Meta: Llama 3.1 8B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3.1-405b-instruct-free" data-value="meta-llama/llama-3.1-405b-instruct:free">Meta: Llama 3.1 405B Instruct (free)</button>
<button id="open-router-model-meta-llama-llama-3.1-405b-instruct" data-value="meta-llama/llama-3.1-405b-instruct">Meta: Llama 3.1 405B Instruct</button>
- <button id="open-router-model-cognitivecomputations-dolphin-llama-3-70b" data-value="cognitivecomputations/dolphin-llama-3-70b">Dolphin Llama 3 70B 🐬</button>
+ <button id="open-router-model-meta-llama-llama-3.1-405b-instruct-nitro" data-value="meta-llama/llama-3.1-405b-instruct:nitro">Meta: Llama 3.1 405B Instruct (nitro)</button>
<button id="open-router-model-mistralai-codestral-mamba" data-value="mistralai/codestral-mamba">Mistral: Codestral Mamba</button>
<button id="open-router-model-mistralai-mistral-nemo" data-value="mistralai/mistral-nemo">Mistral: Mistral Nemo</button>
<button id="open-router-model-openai-gpt-4o-mini-2024-07-18" data-value="openai/gpt-4o-mini-2024-07-18">OpenAI: GPT-4o-mini (2024-07-18)</button>
<button id="open-router-model-openai-gpt-4o-mini" data-value="openai/gpt-4o-mini">OpenAI: GPT-4o-mini</button>
+ <button id="open-router-model-qwen-qwen-2-7b-instruct-free" data-value="qwen/qwen-2-7b-instruct:free">Qwen 2 7B Instruct (free)</button>
<button id="open-router-model-qwen-qwen-2-7b-instruct" data-value="qwen/qwen-2-7b-instruct">Qwen 2 7B Instruct</button>
<button id="open-router-model-google-gemma-2-27b-it" data-value="google/gemma-2-27b-it">Google: Gemma 2 27B</button>
<button id="open-router-model-alpindale-magnum-72b" data-value="alpindale/magnum-72b">Magnum 72B</button>
<button id="open-router-model-nousresearch-hermes-2-theta-llama-3-8b" data-value="nousresearch/hermes-2-theta-llama-3-8b">Nous: Hermes 2 Theta 8B</button>
<button id="open-router-model-google-gemma-2-9b-it-free" data-value="google/gemma-2-9b-it:free">Google: Gemma 2 9B (free)</button>
<button id="open-router-model-google-gemma-2-9b-it" data-value="google/gemma-2-9b-it">Google: Gemma 2 9B</button>
- <button id="open-router-model-openrouter-flavor-of-the-week" data-value="openrouter/flavor-of-the-week">Flavor of The Week</button>
- <button id="open-router-model-sao10k-l3-stheno-8b" data-value="sao10k/l3-stheno-8b">Llama 3 Stheno 8B v3.3 32K</button>
<button id="open-router-model-ai21-jamba-instruct" data-value="ai21/jamba-instruct">AI21: Jamba Instruct</button>
- <button id="open-router-model-01-ai-yi-large" data-value="01-ai/yi-large">Yi Large</button>
- <button id="open-router-model-nvidia-nemotron-4-340b-instruct" data-value="nvidia/nemotron-4-340b-instruct">NVIDIA Nemotron-4 340B Instruct</button>
- <button id="open-router-model-anthropic-claude-3.5-sonnet-beta" data-value="anthropic/claude-3.5-sonnet:beta">Anthropic: Claude 3.5 Sonnet (self-moderated)</button>
- <button id="open-router-model-anthropic-claude-3.5-sonnet" data-value="anthropic/claude-3.5-sonnet">Anthropic: Claude 3.5 Sonnet</button>
+ <button id="open-router-model-anthropic-claude-3.5-sonnet-20240620" data-value="anthropic/claude-3.5-sonnet-20240620">Anthropic: Claude 3.5 Sonnet (2024-06-20)</button>
+ <button id="open-router-model-anthropic-claude-3.5-sonnet-20240620-beta" data-value="anthropic/claude-3.5-sonnet-20240620:beta">Anthropic: Claude 3.5 Sonnet (2024-06-20) (self-moderated)</button>
<button id="open-router-model-sao10k-l3-euryale-70b" data-value="sao10k/l3-euryale-70b">Llama 3 Euryale 70B v2.1</button>
- <button id="open-router-model-qwen-qwen-2-7b-instruct-free" data-value="qwen/qwen-2-7b-instruct:free">Qwen 2 7B Instruct (free)</button>
- <button id="open-router-model-microsoft-phi-3-medium-4k-instruct" data-value="microsoft/phi-3-medium-4k-instruct">Phi-3 Medium 4K Instruct</button>
<button id="open-router-model-cognitivecomputations-dolphin-mixtral-8x22b" data-value="cognitivecomputations/dolphin-mixtral-8x22b">Dolphin 2.9.2 Mixtral 8x22B 🐬</button>
<button id="open-router-model-qwen-qwen-2-72b-instruct" data-value="qwen/qwen-2-72b-instruct">Qwen 2 72B Instruct</button>
- <button id="open-router-model-openchat-openchat-8b" data-value="openchat/openchat-8b">OpenChat 3.6 8B</button>
<button id="open-router-model-nousresearch-hermes-2-pro-llama-3-8b" data-value="nousresearch/hermes-2-pro-llama-3-8b">NousResearch: Hermes 2 Pro - Llama-3 8B</button>
<button id="open-router-model-mistralai-mistral-7b-instruct-v0.3" data-value="mistralai/mistral-7b-instruct-v0.3">Mistral: Mistral 7B Instruct v0.3</button>
+ <button id="open-router-model-mistralai-mistral-7b-instruct-free" data-value="mistralai/mistral-7b-instruct:free">Mistral: Mistral 7B Instruct (free)</button>
<button id="open-router-model-mistralai-mistral-7b-instruct" data-value="mistralai/mistral-7b-instruct">Mistral: Mistral 7B Instruct</button>
+ <button id="open-router-model-mistralai-mistral-7b-instruct-nitro" data-value="mistralai/mistral-7b-instruct:nitro">Mistral: Mistral 7B Instruct (nitro)</button>
<button id="open-router-model-microsoft-phi-3-mini-128k-instruct-free" data-value="microsoft/phi-3-mini-128k-instruct:free">Phi-3 Mini 128K Instruct (free)</button>
<button id="open-router-model-microsoft-phi-3-mini-128k-instruct" data-value="microsoft/phi-3-mini-128k-instruct">Phi-3 Mini 128K Instruct</button>
<button id="open-router-model-microsoft-phi-3-medium-128k-instruct-free" data-value="microsoft/phi-3-medium-128k-instruct:free">Phi-3 Medium 128K Instruct (free)</button>
<button id="open-router-model-microsoft-phi-3-medium-128k-instruct" data-value="microsoft/phi-3-medium-128k-instruct">Phi-3 Medium 128K Instruct</button>
<button id="open-router-model-neversleep-llama-3-lumimaid-70b" data-value="neversleep/llama-3-lumimaid-70b">Llama 3 Lumimaid 70B</button>
- <button id="open-router-model-deepseek-deepseek-coder" data-value="deepseek/deepseek-coder">DeepSeek-Coder-V2</button>
- <button id="open-router-model-deepseek-deepseek-chat" data-value="deepseek/deepseek-chat">DeepSeek-V2 Chat</button>
+ <button id="open-router-model-google-gemini-flash-1.5" data-value="google/gemini-flash-1.5">Google: Gemini Flash 1.5</button>
+ <button id="open-router-model-deepseek-deepseek-chat" data-value="deepseek/deepseek-chat">DeepSeek V2.5</button>
<button id="open-router-model-perplexity-llama-3-sonar-large-32k-online" data-value="perplexity/llama-3-sonar-large-32k-online">Perplexity: Llama3 Sonar 70B Online</button>
<button id="open-router-model-perplexity-llama-3-sonar-large-32k-chat" data-value="perplexity/llama-3-sonar-large-32k-chat">Perplexity: Llama3 Sonar 70B</button>
- <button id="open-router-model-perplexity-llama-3-sonar-small-32k-online" data-value="perplexity/llama-3-sonar-small-32k-online">Perplexity: Llama3 Sonar 8B Online</button>
<button id="open-router-model-perplexity-llama-3-sonar-small-32k-chat" data-value="perplexity/llama-3-sonar-small-32k-chat">Perplexity: Llama3 Sonar 8B</button>
- <button id="open-router-model-google-gemini-flash-1.5" data-value="google/gemini-flash-1.5">Google: Gemini Flash 1.5</button>
<button id="open-router-model-meta-llama-llama-guard-2-8b" data-value="meta-llama/llama-guard-2-8b">Meta: LlamaGuard 2 8B</button>
- <button id="open-router-model-meta-llama-llama-3-70b" data-value="meta-llama/llama-3-70b">Meta: Llama 3 70B (Base)</button>
- <button id="open-router-model-meta-llama-llama-3-8b" data-value="meta-llama/llama-3-8b">Meta: Llama 3 8B (Base)</button>
<button id="open-router-model-openai-gpt-4o-2024-05-13" data-value="openai/gpt-4o-2024-05-13">OpenAI: GPT-4o (2024-05-13)</button>
<button id="open-router-model-openai-gpt-4o" data-value="openai/gpt-4o">OpenAI: GPT-4o</button>
- <button id="open-router-model-allenai-olmo-7b-instruct" data-value="allenai/olmo-7b-instruct">OLMo 7B Instruct</button>
- <button id="open-router-model-qwen-qwen-4b-chat" data-value="qwen/qwen-4b-chat">Qwen 1.5 4B Chat</button>
- <button id="open-router-model-qwen-qwen-7b-chat" data-value="qwen/qwen-7b-chat">Qwen 1.5 7B Chat</button>
- <button id="open-router-model-qwen-qwen-14b-chat" data-value="qwen/qwen-14b-chat">Qwen 1.5 14B Chat</button>
- <button id="open-router-model-qwen-qwen-32b-chat" data-value="qwen/qwen-32b-chat">Qwen 1.5 32B Chat</button>
+ <button id="open-router-model-openai-gpt-4o-extended" data-value="openai/gpt-4o:extended">OpenAI: GPT-4o (extended)</button>
<button id="open-router-model-qwen-qwen-72b-chat" data-value="qwen/qwen-72b-chat">Qwen 1.5 72B Chat</button>
<button id="open-router-model-qwen-qwen-110b-chat" data-value="qwen/qwen-110b-chat">Qwen 1.5 110B Chat</button>
- <button id="open-router-model-meta-llama-llama-3-8b-instruct-free" data-value="meta-llama/llama-3-8b-instruct:free">Meta: Llama 3 8B Instruct (free)</button>
- <button id="open-router-model-neversleep-llama-3-lumimaid-8b-extended" data-value="neversleep/llama-3-lumimaid-8b:extended">Llama 3 Lumimaid 8B (extended)</button>
<button id="open-router-model-neversleep-llama-3-lumimaid-8b" data-value="neversleep/llama-3-lumimaid-8b">Llama 3 Lumimaid 8B</button>
- <button id="open-router-model-snowflake-snowflake-arctic-instruct" data-value="snowflake/snowflake-arctic-instruct">Snowflake: Arctic Instruct</button>
- <button id="open-router-model-fireworks-firellava-13b" data-value="fireworks/firellava-13b">FireLLaVA 13B</button>
- <button id="open-router-model-lynn-soliloquy-l3" data-value="lynn/soliloquy-l3">Lynn: Llama 3 Soliloquy 8B v2</button>
- <button id="open-router-model-meta-llama-llama-3-8b-instruct-extended" data-value="meta-llama/llama-3-8b-instruct:extended">Meta: Llama 3 8B Instruct (extended)</button>
+ <button id="open-router-model-neversleep-llama-3-lumimaid-8b-extended" data-value="neversleep/llama-3-lumimaid-8b:extended">Llama 3 Lumimaid 8B (extended)</button>
<button id="open-router-model-sao10k-fimbulvetr-11b-v2" data-value="sao10k/fimbulvetr-11b-v2">Fimbulvetr 11B v2</button>
- <button id="open-router-model-meta-llama-llama-3-70b-instruct-nitro" data-value="meta-llama/llama-3-70b-instruct:nitro">Meta: Llama 3 70B Instruct (nitro)</button>
- <button id="open-router-model-meta-llama-llama-3-8b-instruct-nitro" data-value="meta-llama/llama-3-8b-instruct:nitro">Meta: Llama 3 8B Instruct (nitro)</button>
<button id="open-router-model-meta-llama-llama-3-70b-instruct" data-value="meta-llama/llama-3-70b-instruct">Meta: Llama 3 70B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3-70b-instruct-nitro" data-value="meta-llama/llama-3-70b-instruct:nitro">Meta: Llama 3 70B Instruct (nitro)</button>
+ <button id="open-router-model-meta-llama-llama-3-8b-instruct-free" data-value="meta-llama/llama-3-8b-instruct:free">Meta: Llama 3 8B Instruct (free)</button>
<button id="open-router-model-meta-llama-llama-3-8b-instruct" data-value="meta-llama/llama-3-8b-instruct">Meta: Llama 3 8B Instruct</button>
+ <button id="open-router-model-meta-llama-llama-3-8b-instruct-nitro" data-value="meta-llama/llama-3-8b-instruct:nitro">Meta: Llama 3 8B Instruct (nitro)</button>
+ <button id="open-router-model-meta-llama-llama-3-8b-instruct-extended" data-value="meta-llama/llama-3-8b-instruct:extended">Meta: Llama 3 8B Instruct (extended)</button>
<button id="open-router-model-mistralai-mixtral-8x22b-instruct" data-value="mistralai/mixtral-8x22b-instruct">Mistral: Mixtral 8x22B Instruct</button>
<button id="open-router-model-microsoft-wizardlm-2-7b" data-value="microsoft/wizardlm-2-7b">WizardLM-2 7B</button>
<button id="open-router-model-microsoft-wizardlm-2-8x22b" data-value="microsoft/wizardlm-2-8x22b">WizardLM-2 8x22B</button>
- <button id="open-router-model-undi95-toppy-m-7b-nitro" data-value="undi95/toppy-m-7b:nitro">Toppy M 7B (nitro)</button>
- <button id="open-router-model-mistralai-mixtral-8x22b" data-value="mistralai/mixtral-8x22b">Mistral: Mixtral 8x22B (base)</button>
<button id="open-router-model-google-gemini-pro-1.5" data-value="google/gemini-pro-1.5">Google: Gemini Pro 1.5</button>
<button id="open-router-model-openai-gpt-4-turbo" data-value="openai/gpt-4-turbo">OpenAI: GPT-4 Turbo</button>
<button id="open-router-model-cohere-command-r-plus" data-value="cohere/command-r-plus">Cohere: Command R+</button>
+ <button id="open-router-model-cohere-command-r-plus-04-2024" data-value="cohere/command-r-plus-04-2024">Cohere: Command R+ (04-2024)</button>
<button id="open-router-model-databricks-dbrx-instruct" data-value="databricks/dbrx-instruct">Databricks: DBRX 132B Instruct</button>
<button id="open-router-model-sophosympatheia-midnight-rose-70b" data-value="sophosympatheia/midnight-rose-70b">Midnight Rose 70B</button>
<button id="open-router-model-cohere-command-r" data-value="cohere/command-r">Cohere: Command R</button>
<button id="open-router-model-cohere-command" data-value="cohere/command">Cohere: Command</button>
- <button id="open-router-model-anthropic-claude-3-haiku-beta" data-value="anthropic/claude-3-haiku:beta">Anthropic: Claude 3 Haiku (self-moderated)</button>
<button id="open-router-model-anthropic-claude-3-haiku" data-value="anthropic/claude-3-haiku">Anthropic: Claude 3 Haiku</button>
- <button id="open-router-model-google-gemma-7b-it-nitro" data-value="google/gemma-7b-it:nitro">Google: Gemma 7B (nitro)</button>
- <button id="open-router-model-mistralai-mistral-7b-instruct-nitro" data-value="mistralai/mistral-7b-instruct:nitro">Mistral: Mistral 7B Instruct (nitro)</button>
- <button id="open-router-model-mistralai-mixtral-8x7b-instruct-nitro" data-value="mistralai/mixtral-8x7b-instruct:nitro">Mixtral 8x7B Instruct (nitro)</button>
- <button id="open-router-model-gryphe-mythomax-l2-13b-nitro" data-value="gryphe/mythomax-l2-13b:nitro">MythoMax 13B (nitro)</button>
- <button id="open-router-model-anthropic-claude-3-sonnet-beta" data-value="anthropic/claude-3-sonnet:beta">Anthropic: Claude 3 Sonnet (self-moderated)</button>
- <button id="open-router-model-anthropic-claude-3-opus-beta" data-value="anthropic/claude-3-opus:beta">Anthropic: Claude 3 Opus (self-moderated)</button>
+ <button id="open-router-model-anthropic-claude-3-haiku-beta" data-value="anthropic/claude-3-haiku:beta">Anthropic: Claude 3 Haiku (self-moderated)</button>
<button id="open-router-model-anthropic-claude-3-sonnet" data-value="anthropic/claude-3-sonnet">Anthropic: Claude 3 Sonnet</button>
+ <button id="open-router-model-anthropic-claude-3-sonnet-beta" data-value="anthropic/claude-3-sonnet:beta">Anthropic: Claude 3 Sonnet (self-moderated)</button>
<button id="open-router-model-anthropic-claude-3-opus" data-value="anthropic/claude-3-opus">Anthropic: Claude 3 Opus</button>
+ <button id="open-router-model-anthropic-claude-3-opus-beta" data-value="anthropic/claude-3-opus:beta">Anthropic: Claude 3 Opus (self-moderated)</button>
+ <button id="open-router-model-cohere-command-r-03-2024" data-value="cohere/command-r-03-2024">Cohere: Command R (03-2024)</button>
<button id="open-router-model-mistralai-mistral-large" data-value="mistralai/mistral-large">Mistral Large</button>
- <button id="open-router-model-google-gemma-7b-it-free" data-value="google/gemma-7b-it:free">Google: Gemma 7B (free)</button>
- <button id="open-router-model-google-gemma-7b-it" data-value="google/gemma-7b-it">Google: Gemma 7B</button>
- <button id="open-router-model-nousresearch-nous-hermes-2-mistral-7b-dpo" data-value="nousresearch/nous-hermes-2-mistral-7b-dpo">Nous: Hermes 2 Mistral 7B DPO</button>
- <button id="open-router-model-meta-llama-codellama-70b-instruct" data-value="meta-llama/codellama-70b-instruct">Meta: CodeLlama 70B Instruct</button>
- <button id="open-router-model-recursal-eagle-7b" data-value="recursal/eagle-7b">RWKV v5: Eagle 7B</button>
<button id="open-router-model-openai-gpt-4-turbo-preview" data-value="openai/gpt-4-turbo-preview">OpenAI: GPT-4 Turbo Preview</button>
<button id="open-router-model-openai-gpt-3.5-turbo-0613" data-value="openai/gpt-3.5-turbo-0613">OpenAI: GPT-3.5 Turbo (older v0613)</button>
- <button id="open-router-model-undi95-remm-slerp-l2-13b-extended" data-value="undi95/remm-slerp-l2-13b:extended">ReMM SLERP 13B (extended)</button>
- <button id="open-router-model-nousresearch-nous-hermes-2-mixtral-8x7b-sft" data-value="nousresearch/nous-hermes-2-mixtral-8x7b-sft">Nous: Hermes 2 Mixtral 8x7B SFT</button>
<button id="open-router-model-nousresearch-nous-hermes-2-mixtral-8x7b-dpo" data-value="nousresearch/nous-hermes-2-mixtral-8x7b-dpo">Nous: Hermes 2 Mixtral 8x7B DPO</button>
<button id="open-router-model-mistralai-mistral-medium" data-value="mistralai/mistral-medium">Mistral Medium</button>
<button id="open-router-model-mistralai-mistral-small" data-value="mistralai/mistral-small">Mistral Small</button>
<button id="open-router-model-mistralai-mistral-tiny" data-value="mistralai/mistral-tiny">Mistral Tiny</button>
- <button id="open-router-model-austism-chronos-hermes-13b" data-value="austism/chronos-hermes-13b">Chronos Hermes 13B v2</button>
- <button id="open-router-model-nousresearch-nous-hermes-yi-34b" data-value="nousresearch/nous-hermes-yi-34b">Nous: Hermes 2 Yi 34B</button>
<button id="open-router-model-mistralai-mistral-7b-instruct-v0.2" data-value="mistralai/mistral-7b-instruct-v0.2">Mistral: Mistral 7B Instruct v0.2</button>
<button id="open-router-model-cognitivecomputations-dolphin-mixtral-8x7b" data-value="cognitivecomputations/dolphin-mixtral-8x7b">Dolphin 2.6 Mixtral 8x7B 🐬</button>
- <button id="open-router-model-google-gemini-pro-vision" data-value="google/gemini-pro-vision">Google: Gemini Pro Vision 1.0</button>
<button id="open-router-model-google-gemini-pro" data-value="google/gemini-pro">Google: Gemini Pro 1.0</button>
- <button id="open-router-model-recursal-rwkv-5-3b-ai-town" data-value="recursal/rwkv-5-3b-ai-town">RWKV v5 3B AI Town</button>
- <button id="open-router-model-rwkv-rwkv-5-world-3b" data-value="rwkv/rwkv-5-world-3b">RWKV v5 World 3B</button>
+ <button id="open-router-model-google-gemini-pro-vision" data-value="google/gemini-pro-vision">Google: Gemini Pro Vision 1.0</button>
<button id="open-router-model-mistralai-mixtral-8x7b-instruct" data-value="mistralai/mixtral-8x7b-instruct">Mixtral 8x7B Instruct</button>
+ <button id="open-router-model-mistralai-mixtral-8x7b-instruct-nitro" data-value="mistralai/mixtral-8x7b-instruct:nitro">Mixtral 8x7B Instruct (nitro)</button>
<button id="open-router-model-mistralai-mixtral-8x7b" data-value="mistralai/mixtral-8x7b">Mixtral 8x7B (base)</button>
- <button id="open-router-model-togethercomputer-stripedhyena-hessian-7b" data-value="togethercomputer/stripedhyena-hessian-7b">StripedHyena Hessian 7B (base)</button>
- <button id="open-router-model-togethercomputer-stripedhyena-nous-7b" data-value="togethercomputer/stripedhyena-nous-7b">StripedHyena Nous 7B</button>
- <button id="open-router-model-koboldai-psyfighter-13b-2" data-value="koboldai/psyfighter-13b-2">Psyfighter v2 13B</button>
+ <button id="open-router-model-gryphe-mythomist-7b-free" data-value="gryphe/mythomist-7b:free">MythoMist 7B (free)</button>
<button id="open-router-model-gryphe-mythomist-7b" data-value="gryphe/mythomist-7b">MythoMist 7B</button>
- <button id="open-router-model-01-ai-yi-6b" data-value="01-ai/yi-6b">Yi 6B (base)</button>
- <button id="open-router-model-01-ai-yi-34b" data-value="01-ai/yi-34b">Yi 34B (base)</button>
- <button id="open-router-model-01-ai-yi-34b-chat" data-value="01-ai/yi-34b-chat">Yi 34B Chat</button>
- <button id="open-router-model-nousresearch-nous-capybara-7b-free" data-value="nousresearch/nous-capybara-7b:free">Nous: Capybara 7B (free)</button>
- <button id="open-router-model-nousresearch-nous-capybara-7b" data-value="nousresearch/nous-capybara-7b">Nous: Capybara 7B</button>
<button id="open-router-model-openchat-openchat-7b-free" data-value="openchat/openchat-7b:free">OpenChat 3.5 7B (free)</button>
<button id="open-router-model-openchat-openchat-7b" data-value="openchat/openchat-7b">OpenChat 3.5 7B</button>
- <button id="open-router-model-gryphe-mythomist-7b-free" data-value="gryphe/mythomist-7b:free">MythoMist 7B (free)</button>
<button id="open-router-model-neversleep-noromaid-20b" data-value="neversleep/noromaid-20b">Noromaid 20B</button>
- <button id="open-router-model-intel-neural-chat-7b" data-value="intel/neural-chat-7b">Neural Chat 7B v3.1</button>
- <button id="open-router-model-anthropic-claude-2.1-beta" data-value="anthropic/claude-2.1:beta">Anthropic: Claude v2.1 (self-moderated)</button>
- <button id="open-router-model-anthropic-claude-2-beta" data-value="anthropic/claude-2:beta">Anthropic: Claude v2 (self-moderated)</button>
<button id="open-router-model-anthropic-claude-instant-1.1" data-value="anthropic/claude-instant-1.1">Anthropic: Claude Instant v1.1</button>
<button id="open-router-model-anthropic-claude-2.1" data-value="anthropic/claude-2.1">Anthropic: Claude v2.1</button>
+ <button id="open-router-model-anthropic-claude-2.1-beta" data-value="anthropic/claude-2.1:beta">Anthropic: Claude v2.1 (self-moderated)</button>
<button id="open-router-model-anthropic-claude-2" data-value="anthropic/claude-2">Anthropic: Claude v2</button>
+ <button id="open-router-model-anthropic-claude-2-beta" data-value="anthropic/claude-2:beta">Anthropic: Claude v2 (self-moderated)</button>
<button id="open-router-model-teknium-openhermes-2.5-mistral-7b" data-value="teknium/openhermes-2.5-mistral-7b">OpenHermes 2.5 Mistral 7B</button>
<button id="open-router-model-openai-gpt-4-vision-preview" data-value="openai/gpt-4-vision-preview">OpenAI: GPT-4 Vision</button>
<button id="open-router-model-lizpreciatior-lzlv-70b-fp16-hf" data-value="lizpreciatior/lzlv-70b-fp16-hf">lzlv 70B</button>
- <button id="open-router-model-undi95-toppy-m-7b-free" data-value="undi95/toppy-m-7b:free">Toppy M 7B (free)</button>
<button id="open-router-model-alpindale-goliath-120b" data-value="alpindale/goliath-120b">Goliath 120B</button>
+ <button id="open-router-model-undi95-toppy-m-7b-free" data-value="undi95/toppy-m-7b:free">Toppy M 7B (free)</button>
<button id="open-router-model-undi95-toppy-m-7b" data-value="undi95/toppy-m-7b">Toppy M 7B</button>
+ <button id="open-router-model-undi95-toppy-m-7b-nitro" data-value="undi95/toppy-m-7b:nitro">Toppy M 7B (nitro)</button>
<button id="open-router-model-openrouter-auto" data-value="openrouter/auto">Auto (best for prompt)</button>
<button id="open-router-model-openai-gpt-4-1106-preview" data-value="openai/gpt-4-1106-preview">OpenAI: GPT-4 Turbo (older v1106)</button>
<button id="open-router-model-openai-gpt-3.5-turbo-1106" data-value="openai/gpt-3.5-turbo-1106">OpenAI: GPT-3.5 Turbo 16k (older v1106)</button>
- <button id="open-router-model-huggingfaceh4-zephyr-7b-beta-free" data-value="huggingfaceh4/zephyr-7b-beta:free">Hugging Face: Zephyr 7B (free)</button>
<button id="open-router-model-google-palm-2-codechat-bison-32k" data-value="google/palm-2-codechat-bison-32k">Google: PaLM 2 Code Chat 32k</button>
<button id="open-router-model-google-palm-2-chat-bison-32k" data-value="google/palm-2-chat-bison-32k">Google: PaLM 2 Chat 32k</button>
- <button id="open-router-model-teknium-openhermes-2-mistral-7b" data-value="teknium/openhermes-2-mistral-7b">OpenHermes 2 Mistral 7B</button>
- <button id="open-router-model-open-orca-mistral-7b-openorca" data-value="open-orca/mistral-7b-openorca">Mistral OpenOrca 7B</button>
<button id="open-router-model-jondurbin-airoboros-l2-70b" data-value="jondurbin/airoboros-l2-70b">Airoboros 70B</button>
- <button id="open-router-model-gryphe-mythomax-l2-13b-extended" data-value="gryphe/mythomax-l2-13b:extended">MythoMax 13B (extended)</button>
<button id="open-router-model-xwin-lm-xwin-lm-70b" data-value="xwin-lm/xwin-lm-70b">Xwin 70B</button>
- <button id="open-router-model-mistralai-mistral-7b-instruct-free" data-value="mistralai/mistral-7b-instruct:free">Mistral: Mistral 7B Instruct (free)</button>
<button id="open-router-model-mistralai-mistral-7b-instruct-v0.1" data-value="mistralai/mistral-7b-instruct-v0.1">Mistral: Mistral 7B Instruct v0.1</button>
<button id="open-router-model-openai-gpt-3.5-turbo-instruct" data-value="openai/gpt-3.5-turbo-instruct">OpenAI: GPT-3.5 Turbo Instruct</button>
<button id="open-router-model-pygmalionai-mythalion-13b" data-value="pygmalionai/mythalion-13b">Pygmalion: Mythalion 13B</button>
@@ -223,25 +242,26 @@
<button id="open-router-model-openai-gpt-4-32k" data-value="openai/gpt-4-32k">OpenAI: GPT-4 32k</button>
<button id="open-router-model-openai-gpt-3.5-turbo-16k" data-value="openai/gpt-3.5-turbo-16k">OpenAI: GPT-3.5 Turbo 16k</button>
<button id="open-router-model-nousresearch-nous-hermes-llama2-13b" data-value="nousresearch/nous-hermes-llama2-13b">Nous: Hermes 13B</button>
- <button id="open-router-model-phind-phind-codellama-34b" data-value="phind/phind-codellama-34b">Phind: CodeLlama 34B v2</button>
- <button id="open-router-model-meta-llama-codellama-34b-instruct" data-value="meta-llama/codellama-34b-instruct">Meta: CodeLlama 34B Instruct</button>
+ <button id="open-router-model-huggingfaceh4-zephyr-7b-beta-free" data-value="huggingfaceh4/zephyr-7b-beta:free">Hugging Face: Zephyr 7B (free)</button>
<button id="open-router-model-mancer-weaver" data-value="mancer/weaver">Mancer: Weaver (alpha)</button>
- <button id="open-router-model-anthropic-claude-instant-1-beta" data-value="anthropic/claude-instant-1:beta">Anthropic: Claude Instant v1 (self-moderated)</button>
- <button id="open-router-model-anthropic-claude-2.0-beta" data-value="anthropic/claude-2.0:beta">Anthropic: Claude v2.0 (self-moderated)</button>
<button id="open-router-model-anthropic-claude-instant-1.0" data-value="anthropic/claude-instant-1.0">Anthropic: Claude Instant v1.0</button>
<button id="open-router-model-anthropic-claude-1.2" data-value="anthropic/claude-1.2">Anthropic: Claude v1.2</button>
<button id="open-router-model-anthropic-claude-1" data-value="anthropic/claude-1">Anthropic: Claude v1</button>
<button id="open-router-model-anthropic-claude-instant-1" data-value="anthropic/claude-instant-1">Anthropic: Claude Instant v1</button>
+ <button id="open-router-model-anthropic-claude-instant-1-beta" data-value="anthropic/claude-instant-1:beta">Anthropic: Claude Instant v1 (self-moderated)</button>
<button id="open-router-model-anthropic-claude-2.0" data-value="anthropic/claude-2.0">Anthropic: Claude v2.0</button>
+ <button id="open-router-model-anthropic-claude-2.0-beta" data-value="anthropic/claude-2.0:beta">Anthropic: Claude v2.0 (self-moderated)</button>
<button id="open-router-model-undi95-remm-slerp-l2-13b" data-value="undi95/remm-slerp-l2-13b">ReMM SLERP 13B</button>
+ <button id="open-router-model-undi95-remm-slerp-l2-13b-extended" data-value="undi95/remm-slerp-l2-13b:extended">ReMM SLERP 13B (extended)</button>
<button id="open-router-model-google-palm-2-codechat-bison" data-value="google/palm-2-codechat-bison">Google: PaLM 2 Code Chat</button>
<button id="open-router-model-google-palm-2-chat-bison" data-value="google/palm-2-chat-bison">Google: PaLM 2 Chat</button>
+ <button id="open-router-model-gryphe-mythomax-l2-13b-free" data-value="gryphe/mythomax-l2-13b:free">MythoMax 13B (free)</button>
<button id="open-router-model-gryphe-mythomax-l2-13b" data-value="gryphe/mythomax-l2-13b">MythoMax 13B</button>
- <button id="open-router-model-meta-llama-llama-2-70b-chat" data-value="meta-llama/llama-2-70b-chat">Meta: Llama v2 70B Chat</button>
+ <button id="open-router-model-gryphe-mythomax-l2-13b-nitro" data-value="gryphe/mythomax-l2-13b:nitro">MythoMax 13B (nitro)</button>
+ <button id="open-router-model-gryphe-mythomax-l2-13b-extended" data-value="gryphe/mythomax-l2-13b:extended">MythoMax 13B (extended)</button>
<button id="open-router-model-meta-llama-llama-2-13b-chat" data-value="meta-llama/llama-2-13b-chat">Meta: Llama v2 13B Chat</button>
<button id="open-router-model-openai-gpt-4-0314" data-value="openai/gpt-4-0314">OpenAI: GPT-4 (older v0314)</button>
<button id="open-router-model-openai-gpt-4" data-value="openai/gpt-4">OpenAI: GPT-4</button>
- <button id="open-router-model-openai-gpt-3.5-turbo-0301" data-value="openai/gpt-3.5-turbo-0301">OpenAI: GPT-3.5 Turbo (older v0301)</button>
<button id="open-router-model-openai-gpt-3.5-turbo-0125" data-value="openai/gpt-3.5-turbo-0125">OpenAI: GPT-3.5 Turbo 16k</button>
<button id="open-router-model-openai-gpt-3.5-turbo" data-value="openai/gpt-3.5-turbo">OpenAI: GPT-3.5 Turbo</button>
</div>
diff --git a/public/script.js b/public/script.js
index 63505cb..00762ff 100644
--- a/public/script.js
+++ b/public/script.js
@@ -87,155 +87,174 @@ fetchDefaultModel();
"Llama 3.1 70B": "llama-3.1-70b-versatile",
"Llama 3.1 405B": "llama-3.1-405b-reasoning",
// Open Router Models
+ "Lumimaid v0.2 70B": "neversleep/llama-3.1-lumimaid-70b",
+ "Magnum v4 72B": "anthracite-org/magnum-v4-72b",
+ "Anthropic: Claude 3.5 Sonnet": "anthropic/claude-3.5-sonnet",
+ "Anthropic: Claude 3.5 Sonnet (self-moderated)": "anthropic/claude-3.5-sonnet:beta",
+ "xAI: Grok Beta": "x-ai/grok-beta",
+ "Ministral 8B": "mistralai/ministral-8b",
+ "Ministral 3B": "mistralai/ministral-3b",
+ "Qwen2.5 7B Instruct": "qwen/qwen-2.5-7b-instruct",
+ "NVIDIA: Llama 3.1 Nemotron 70B Instruct": "nvidia/llama-3.1-nemotron-70b-instruct",
+ "Inflection: Inflection 3 Pi": "inflection/inflection-3-pi",
+ "Inflection: Inflection 3 Productivity": "inflection/inflection-3-productivity",
+ "Google: Gemini 1.5 Flash-8B": "google/gemini-flash-1.5-8b",
+ "Liquid: LFM 40B MoE (free)": "liquid/lfm-40b:free",
+ "Liquid: LFM 40B MoE": "liquid/lfm-40b",
+ "Rocinante 12B": "thedrummer/rocinante-12b",
+ "EVA Qwen2.5 14B": "eva-unit-01/eva-qwen-2.5-14b",
+ "Magnum v2 72B": "anthracite-org/magnum-v2-72b",
+ "Meta: Llama 3.2 3B Instruct (free)": "meta-llama/llama-3.2-3b-instruct:free",
+ "Meta: Llama 3.2 3B Instruct": "meta-llama/llama-3.2-3b-instruct",
+ "Meta: Llama 3.2 1B Instruct (free)": "meta-llama/llama-3.2-1b-instruct:free",
+ "Meta: Llama 3.2 1B Instruct": "meta-llama/llama-3.2-1b-instruct",
+ "Meta: Llama 3.2 90B Vision Instruct": "meta-llama/llama-3.2-90b-vision-instruct",
+ "Meta: Llama 3.2 11B Vision Instruct (free)": "meta-llama/llama-3.2-11b-vision-instruct:free",
+ "Meta: Llama 3.2 11B Vision Instruct": "meta-llama/llama-3.2-11b-vision-instruct",
+ "Qwen2.5 72B Instruct": "qwen/qwen-2.5-72b-instruct",
+ "Qwen2-VL 72B Instruct": "qwen/qwen-2-vl-72b-instruct",
+ "Lumimaid v0.2 8B": "neversleep/llama-3.1-lumimaid-8b",
+ "OpenAI: o1-mini (2024-09-12)": "openai/o1-mini-2024-09-12",
+ "OpenAI: o1-mini": "openai/o1-mini",
+ "OpenAI: o1-preview (2024-09-12)": "openai/o1-preview-2024-09-12",
+ "OpenAI: o1-preview": "openai/o1-preview",
+ "Mistral: Pixtral 12B": "mistralai/pixtral-12b",
+ "Cohere: Command R+ (08-2024)": "cohere/command-r-plus-08-2024",
+ "Cohere: Command R (08-2024)": "cohere/command-r-08-2024",
+ "Qwen2-VL 7B Instruct": "qwen/qwen-2-vl-7b-instruct",
+ "Google: Gemini Flash 8B 1.5 Experimental": "google/gemini-flash-1.5-8b-exp",
+ "Llama 3.1 Euryale 70B v2.2": "sao10k/l3.1-euryale-70b",
+ "Google: Gemini Flash 1.5 Experimental": "google/gemini-flash-1.5-exp",
+ "AI21: Jamba 1.5 Large": "ai21/jamba-1-5-large",
+ "AI21: Jamba 1.5 Mini": "ai21/jamba-1-5-mini",
+ "Phi-3.5 Mini 128K Instruct": "microsoft/phi-3.5-mini-128k-instruct",
+ "Nous: Hermes 3 70B Instruct": "nousresearch/hermes-3-llama-3.1-70b",
+ "Nous: Hermes 3 405B Instruct (free)": "nousresearch/hermes-3-llama-3.1-405b:free",
+ "Nous: Hermes 3 405B Instruct": "nousresearch/hermes-3-llama-3.1-405b",
+ "Nous: Hermes 3 405B Instruct (extended)": "nousresearch/hermes-3-llama-3.1-405b:extended",
+ "Perplexity: Llama 3.1 Sonar 405B Online": "perplexity/llama-3.1-sonar-huge-128k-online",
+ "OpenAI: ChatGPT-4o": "openai/chatgpt-4o-latest",
+ "Llama 3 8B Lunaris": "sao10k/l3-lunaris-8b",
+ "Mistral Nemo 12B Starcannon": "aetherwiing/mn-starcannon-12b",
+ "OpenAI: GPT-4o (2024-08-06)": "openai/gpt-4o-2024-08-06",
+ "Meta: Llama 3.1 405B (base)": "meta-llama/llama-3.1-405b",
+ "Mistral Nemo 12B Celeste": "nothingiisreal/mn-celeste-12b",
+ "Google: Gemini Pro 1.5 Experimental": "google/gemini-pro-1.5-exp",
+ "Perplexity: Llama 3.1 Sonar 70B Online": "perplexity/llama-3.1-sonar-large-128k-online",
+ "Perplexity: Llama 3.1 Sonar 70B": "perplexity/llama-3.1-sonar-large-128k-chat",
+ "Perplexity: Llama 3.1 Sonar 8B Online": "perplexity/llama-3.1-sonar-small-128k-online",
+ "Perplexity: Llama 3.1 Sonar 8B": "perplexity/llama-3.1-sonar-small-128k-chat",
+ "Meta: Llama 3.1 70B Instruct (free)": "meta-llama/llama-3.1-70b-instruct:free",
"Meta: Llama 3.1 70B Instruct": "meta-llama/llama-3.1-70b-instruct",
+ "Meta: Llama 3.1 70B Instruct (nitro)": "meta-llama/llama-3.1-70b-instruct:nitro",
+ "Meta: Llama 3.1 8B Instruct (free)": "meta-llama/llama-3.1-8b-instruct:free",
"Meta: Llama 3.1 8B Instruct": "meta-llama/llama-3.1-8b-instruct",
+ "Meta: Llama 3.1 405B Instruct (free)": "meta-llama/llama-3.1-405b-instruct:free",
"Meta: Llama 3.1 405B Instruct": "meta-llama/llama-3.1-405b-instruct",
- "Dolphin Llama 3 70B \ud83d\udc2c": "cognitivecomputations/dolphin-llama-3-70b",
+ "Meta: Llama 3.1 405B Instruct (nitro)": "meta-llama/llama-3.1-405b-instruct:nitro",
"Mistral: Codestral Mamba": "mistralai/codestral-mamba",
"Mistral: Mistral Nemo": "mistralai/mistral-nemo",
"OpenAI: GPT-4o-mini (2024-07-18)": "openai/gpt-4o-mini-2024-07-18",
"OpenAI: GPT-4o-mini": "openai/gpt-4o-mini",
+ "Qwen 2 7B Instruct (free)": "qwen/qwen-2-7b-instruct:free",
"Qwen 2 7B Instruct": "qwen/qwen-2-7b-instruct",
"Google: Gemma 2 27B": "google/gemma-2-27b-it",
"Magnum 72B": "alpindale/magnum-72b",
"Nous: Hermes 2 Theta 8B": "nousresearch/hermes-2-theta-llama-3-8b",
"Google: Gemma 2 9B (free)": "google/gemma-2-9b-it:free",
"Google: Gemma 2 9B": "google/gemma-2-9b-it",
- "Flavor of The Week": "openrouter/flavor-of-the-week",
- "Llama 3 Stheno 8B v3.3 32K": "sao10k/l3-stheno-8b",
"AI21: Jamba Instruct": "ai21/jamba-instruct",
- "Yi Large": "01-ai/yi-large",
- "NVIDIA Nemotron-4 340B Instruct": "nvidia/nemotron-4-340b-instruct",
- "Anthropic: Claude 3.5 Sonnet (self-moderated)": "anthropic/claude-3.5-sonnet:beta",
- "Anthropic: Claude 3.5 Sonnet": "anthropic/claude-3.5-sonnet",
+ "Anthropic: Claude 3.5 Sonnet (2024-06-20)": "anthropic/claude-3.5-sonnet-20240620",
+ "Anthropic: Claude 3.5 Sonnet (2024-06-20) (self-moderated)": "anthropic/claude-3.5-sonnet-20240620:beta",
"Llama 3 Euryale 70B v2.1": "sao10k/l3-euryale-70b",
- "Qwen 2 7B Instruct (free)": "qwen/qwen-2-7b-instruct:free",
- "Phi-3 Medium 4K Instruct": "microsoft/phi-3-medium-4k-instruct",
"Dolphin 2.9.2 Mixtral 8x22B \ud83d\udc2c": "cognitivecomputations/dolphin-mixtral-8x22b",
"Qwen 2 72B Instruct": "qwen/qwen-2-72b-instruct",
- "OpenChat 3.6 8B": "openchat/openchat-8b",
"NousResearch: Hermes 2 Pro - Llama-3 8B": "nousresearch/hermes-2-pro-llama-3-8b",
"Mistral: Mistral 7B Instruct v0.3": "mistralai/mistral-7b-instruct-v0.3",
+ "Mistral: Mistral 7B Instruct (free)": "mistralai/mistral-7b-instruct:free",
"Mistral: Mistral 7B Instruct": "mistralai/mistral-7b-instruct",
+ "Mistral: Mistral 7B Instruct (nitro)": "mistralai/mistral-7b-instruct:nitro",
"Phi-3 Mini 128K Instruct (free)": "microsoft/phi-3-mini-128k-instruct:free",
"Phi-3 Mini 128K Instruct": "microsoft/phi-3-mini-128k-instruct",
"Phi-3 Medium 128K Instruct (free)": "microsoft/phi-3-medium-128k-instruct:free",
"Phi-3 Medium 128K Instruct": "microsoft/phi-3-medium-128k-instruct",
"Llama 3 Lumimaid 70B": "neversleep/llama-3-lumimaid-70b",
- "DeepSeek-Coder-V2": "deepseek/deepseek-coder",
- "DeepSeek-V2 Chat": "deepseek/deepseek-chat",
+ "Google: Gemini Flash 1.5": "google/gemini-flash-1.5",
+ "DeepSeek V2.5": "deepseek/deepseek-chat",
"Perplexity: Llama3 Sonar 70B Online": "perplexity/llama-3-sonar-large-32k-online",
"Perplexity: Llama3 Sonar 70B": "perplexity/llama-3-sonar-large-32k-chat",
- "Perplexity: Llama3 Sonar 8B Online": "perplexity/llama-3-sonar-small-32k-online",
"Perplexity: Llama3 Sonar 8B": "perplexity/llama-3-sonar-small-32k-chat",
- "Google: Gemini Flash 1.5": "google/gemini-flash-1.5",
"Meta: LlamaGuard 2 8B": "meta-llama/llama-guard-2-8b",
- "Meta: Llama 3 70B (Base)": "meta-llama/llama-3-70b",
- "Meta: Llama 3 8B (Base)": "meta-llama/llama-3-8b",
"OpenAI: GPT-4o (2024-05-13)": "openai/gpt-4o-2024-05-13",
"OpenAI: GPT-4o": "openai/gpt-4o",
- "OLMo 7B Instruct": "allenai/olmo-7b-instruct",
- "Qwen 1.5 4B Chat": "qwen/qwen-4b-chat",
- "Qwen 1.5 7B Chat": "qwen/qwen-7b-chat",
- "Qwen 1.5 14B Chat": "qwen/qwen-14b-chat",
- "Qwen 1.5 32B Chat": "qwen/qwen-32b-chat",
+ "OpenAI: GPT-4o (extended)": "openai/gpt-4o:extended",
"Qwen 1.5 72B Chat": "qwen/qwen-72b-chat",
"Qwen 1.5 110B Chat": "qwen/qwen-110b-chat",
- "Meta: Llama 3 8B Instruct (free)": "meta-llama/llama-3-8b-instruct:free",
- "Llama 3 Lumimaid 8B (extended)": "neversleep/llama-3-lumimaid-8b:extended",
"Llama 3 Lumimaid 8B": "neversleep/llama-3-lumimaid-8b",
- "Snowflake: Arctic Instruct": "snowflake/snowflake-arctic-instruct",
- "FireLLaVA 13B": "fireworks/firellava-13b",
- "Lynn: Llama 3 Soliloquy 8B v2": "lynn/soliloquy-l3",
- "Meta: Llama 3 8B Instruct (extended)": "meta-llama/llama-3-8b-instruct:extended",
+ "Llama 3 Lumimaid 8B (extended)": "neversleep/llama-3-lumimaid-8b:extended",
"Fimbulvetr 11B v2": "sao10k/fimbulvetr-11b-v2",
- "Meta: Llama 3 70B Instruct (nitro)": "meta-llama/llama-3-70b-instruct:nitro",
- "Meta: Llama 3 8B Instruct (nitro)": "meta-llama/llama-3-8b-instruct:nitro",
"Meta: Llama 3 70B Instruct": "meta-llama/llama-3-70b-instruct",
+ "Meta: Llama 3 70B Instruct (nitro)": "meta-llama/llama-3-70b-instruct:nitro",
+ "Meta: Llama 3 8B Instruct (free)": "meta-llama/llama-3-8b-instruct:free",
"Meta: Llama 3 8B Instruct": "meta-llama/llama-3-8b-instruct",
+ "Meta: Llama 3 8B Instruct (nitro)": "meta-llama/llama-3-8b-instruct:nitro",
+ "Meta: Llama 3 8B Instruct (extended)": "meta-llama/llama-3-8b-instruct:extended",
"Mistral: Mixtral 8x22B Instruct": "mistralai/mixtral-8x22b-instruct",
"WizardLM-2 7B": "microsoft/wizardlm-2-7b",
"WizardLM-2 8x22B": "microsoft/wizardlm-2-8x22b",
- "Toppy M 7B (nitro)": "undi95/toppy-m-7b:nitro",
- "Mistral: Mixtral 8x22B (base)": "mistralai/mixtral-8x22b",
"Google: Gemini Pro 1.5": "google/gemini-pro-1.5",
"OpenAI: GPT-4 Turbo": "openai/gpt-4-turbo",
"Cohere: Command R+": "cohere/command-r-plus",
+ "Cohere: Command R+ (04-2024)": "cohere/command-r-plus-04-2024",
"Databricks: DBRX 132B Instruct": "databricks/dbrx-instruct",
"Midnight Rose 70B": "sophosympatheia/midnight-rose-70b",
"Cohere: Command R": "cohere/command-r",
"Cohere: Command": "cohere/command",
- "Anthropic: Claude 3 Haiku (self-moderated)": "anthropic/claude-3-haiku:beta",
"Anthropic: Claude 3 Haiku": "anthropic/claude-3-haiku",
- "Google: Gemma 7B (nitro)": "google/gemma-7b-it:nitro",
- "Mistral: Mistral 7B Instruct (nitro)": "mistralai/mistral-7b-instruct:nitro",
- "Mixtral 8x7B Instruct (nitro)": "mistralai/mixtral-8x7b-instruct:nitro",
- "MythoMax 13B (nitro)": "gryphe/mythomax-l2-13b:nitro",
- "Anthropic: Claude 3 Sonnet (self-moderated)": "anthropic/claude-3-sonnet:beta",
- "Anthropic: Claude 3 Opus (self-moderated)": "anthropic/claude-3-opus:beta",
+ "Anthropic: Claude 3 Haiku (self-moderated)": "anthropic/claude-3-haiku:beta",
"Anthropic: Claude 3 Sonnet": "anthropic/claude-3-sonnet",
+ "Anthropic: Claude 3 Sonnet (self-moderated)": "anthropic/claude-3-sonnet:beta",
"Anthropic: Claude 3 Opus": "anthropic/claude-3-opus",
+ "Anthropic: Claude 3 Opus (self-moderated)": "anthropic/claude-3-opus:beta",
+ "Cohere: Command R (03-2024)": "cohere/command-r-03-2024",
"Mistral Large": "mistralai/mistral-large",
- "Google: Gemma 7B (free)": "google/gemma-7b-it:free",
- "Google: Gemma 7B": "google/gemma-7b-it",
- "Nous: Hermes 2 Mistral 7B DPO": "nousresearch/nous-hermes-2-mistral-7b-dpo",
- "Meta: CodeLlama 70B Instruct": "meta-llama/codellama-70b-instruct",
- "RWKV v5: Eagle 7B": "recursal/eagle-7b",
"OpenAI: GPT-4 Turbo Preview": "openai/gpt-4-turbo-preview",
"OpenAI: GPT-3.5 Turbo (older v0613)": "openai/gpt-3.5-turbo-0613",
- "ReMM SLERP 13B (extended)": "undi95/remm-slerp-l2-13b:extended",
- "Nous: Hermes 2 Mixtral 8x7B SFT": "nousresearch/nous-hermes-2-mixtral-8x7b-sft",
"Nous: Hermes 2 Mixtral 8x7B DPO": "nousresearch/nous-hermes-2-mixtral-8x7b-dpo",
"Mistral Medium": "mistralai/mistral-medium",
"Mistral Small": "mistralai/mistral-small",
"Mistral Tiny": "mistralai/mistral-tiny",
- "Chronos Hermes 13B v2": "austism/chronos-hermes-13b",
- "Nous: Hermes 2 Yi 34B": "nousresearch/nous-hermes-yi-34b",
"Mistral: Mistral 7B Instruct v0.2": "mistralai/mistral-7b-instruct-v0.2",
"Dolphin 2.6 Mixtral 8x7B \ud83d\udc2c": "cognitivecomputations/dolphin-mixtral-8x7b",
- "Google: Gemini Pro Vision 1.0": "google/gemini-pro-vision",
"Google: Gemini Pro 1.0": "google/gemini-pro",
- "RWKV v5 3B AI Town": "recursal/rwkv-5-3b-ai-town",
- "RWKV v5 World 3B": "rwkv/rwkv-5-world-3b",
+ "Google: Gemini Pro Vision 1.0": "google/gemini-pro-vision",
"Mixtral 8x7B Instruct": "mistralai/mixtral-8x7b-instruct",
+ "Mixtral 8x7B Instruct (nitro)": "mistralai/mixtral-8x7b-instruct:nitro",
"Mixtral 8x7B (base)": "mistralai/mixtral-8x7b",
- "StripedHyena Hessian 7B (base)": "togethercomputer/stripedhyena-hessian-7b",
- "StripedHyena Nous 7B": "togethercomputer/stripedhyena-nous-7b",
- "Psyfighter v2 13B": "koboldai/psyfighter-13b-2",
+ "MythoMist 7B (free)": "gryphe/mythomist-7b:free",
"MythoMist 7B": "gryphe/mythomist-7b",
- "Yi 6B (base)": "01-ai/yi-6b",
- "Yi 34B (base)": "01-ai/yi-34b",
- "Yi 34B Chat": "01-ai/yi-34b-chat",
- "Nous: Capybara 7B (free)": "nousresearch/nous-capybara-7b:free",
- "Nous: Capybara 7B": "nousresearch/nous-capybara-7b",
"OpenChat 3.5 7B (free)": "openchat/openchat-7b:free",
"OpenChat 3.5 7B": "openchat/openchat-7b",
- "MythoMist 7B (free)": "gryphe/mythomist-7b:free",
"Noromaid 20B": "neversleep/noromaid-20b",
- "Neural Chat 7B v3.1": "intel/neural-chat-7b",
- "Anthropic: Claude v2.1 (self-moderated)": "anthropic/claude-2.1:beta",
- "Anthropic: Claude v2 (self-moderated)": "anthropic/claude-2:beta",
"Anthropic: Claude Instant v1.1": "anthropic/claude-instant-1.1",
"Anthropic: Claude v2.1": "anthropic/claude-2.1",
+ "Anthropic: Claude v2.1 (self-moderated)": "anthropic/claude-2.1:beta",
"Anthropic: Claude v2": "anthropic/claude-2",
+ "Anthropic: Claude v2 (self-moderated)": "anthropic/claude-2:beta",
"OpenHermes 2.5 Mistral 7B": "teknium/openhermes-2.5-mistral-7b",
"OpenAI: GPT-4 Vision": "openai/gpt-4-vision-preview",
"lzlv 70B": "lizpreciatior/lzlv-70b-fp16-hf",
- "Toppy M 7B (free)": "undi95/toppy-m-7b:free",
"Goliath 120B": "alpindale/goliath-120b",
+ "Toppy M 7B (free)": "undi95/toppy-m-7b:free",
"Toppy M 7B": "undi95/toppy-m-7b",
+ "Toppy M 7B (nitro)": "undi95/toppy-m-7b:nitro",
"Auto (best for prompt)": "openrouter/auto",
"OpenAI: GPT-4 Turbo (older v1106)": "openai/gpt-4-1106-preview",
"OpenAI: GPT-3.5 Turbo 16k (older v1106)": "openai/gpt-3.5-turbo-1106",
- "Hugging Face: Zephyr 7B (free)": "huggingfaceh4/zephyr-7b-beta:free",
"Google: PaLM 2 Code Chat 32k": "google/palm-2-codechat-bison-32k",
"Google: PaLM 2 Chat 32k": "google/palm-2-chat-bison-32k",
- "OpenHermes 2 Mistral 7B": "teknium/openhermes-2-mistral-7b",
- "Mistral OpenOrca 7B": "open-orca/mistral-7b-openorca",
"Airoboros 70B": "jondurbin/airoboros-l2-70b",
- "MythoMax 13B (extended)": "gryphe/mythomax-l2-13b:extended",
"Xwin 70B": "xwin-lm/xwin-lm-70b",
- "Mistral: Mistral 7B Instruct (free)": "mistralai/mistral-7b-instruct:free",
"Mistral: Mistral 7B Instruct v0.1": "mistralai/mistral-7b-instruct-v0.1",
"OpenAI: GPT-3.5 Turbo Instruct": "openai/gpt-3.5-turbo-instruct",
"Pygmalion: Mythalion 13B": "pygmalionai/mythalion-13b",
@@ -243,25 +262,26 @@ fetchDefaultModel();
"OpenAI: GPT-4 32k": "openai/gpt-4-32k",
"OpenAI: GPT-3.5 Turbo 16k": "openai/gpt-3.5-turbo-0125",
"Nous: Hermes 13B": "nousresearch/nous-hermes-llama2-13b",
- "Phind: CodeLlama 34B v2": "phind/phind-codellama-34b",
- "Meta: CodeLlama 34B Instruct": "meta-llama/codellama-34b-instruct",
+ "Hugging Face: Zephyr 7B (free)": "huggingfaceh4/zephyr-7b-beta:free",
"Mancer: Weaver (alpha)": "mancer/weaver",
- "Anthropic: Claude Instant v1 (self-moderated)": "anthropic/claude-instant-1:beta",
- "Anthropic: Claude v2.0 (self-moderated)": "anthropic/claude-2.0:beta",
"Anthropic: Claude Instant v1.0": "anthropic/claude-instant-1.0",
"Anthropic: Claude v1.2": "anthropic/claude-1.2",
"Anthropic: Claude v1": "anthropic/claude-1",
"Anthropic: Claude Instant v1": "anthropic/claude-instant-1",
+ "Anthropic: Claude Instant v1 (self-moderated)": "anthropic/claude-instant-1:beta",
"Anthropic: Claude v2.0": "anthropic/claude-2.0",
+ "Anthropic: Claude v2.0 (self-moderated)": "anthropic/claude-2.0:beta",
"ReMM SLERP 13B": "undi95/remm-slerp-l2-13b",
+ "ReMM SLERP 13B (extended)": "undi95/remm-slerp-l2-13b:extended",
"Google: PaLM 2 Code Chat": "google/palm-2-codechat-bison",
"Google: PaLM 2 Chat": "google/palm-2-chat-bison",
+ "MythoMax 13B (free)": "gryphe/mythomax-l2-13b:free",
"MythoMax 13B": "gryphe/mythomax-l2-13b",
- "Meta: Llama v2 70B Chat": "meta-llama/llama-2-70b-chat",
+ "MythoMax 13B (nitro)": "gryphe/mythomax-l2-13b:nitro",
+ "MythoMax 13B (extended)": "gryphe/mythomax-l2-13b:extended",
"Meta: Llama v2 13B Chat": "meta-llama/llama-2-13b-chat",
"OpenAI: GPT-4 (older v0314)": "openai/gpt-4-0314",
"OpenAI: GPT-4": "openai/gpt-4",
- "OpenAI: GPT-3.5 Turbo (older v0301)": "openai/gpt-3.5-turbo-0301",
"OpenAI: GPT-3.5 Turbo": "openai/gpt-3.5-turbo"
};
@@ -305,155 +325,174 @@ fetchDefaultModel();
"llama-3.1-70b-versatile": "Llama 3.1 70B",
"llama-3.1-405b-reasoning": "Llama 3.1 405B",
// Open Router Models
+ "neversleep/llama-3.1-lumimaid-70b": "Lumimaid v0.2 70B",
+ "anthracite-org/magnum-v4-72b": "Magnum v4 72B",
+ "anthropic/claude-3.5-sonnet": "Anthropic: Claude 3.5 Sonnet",
+ "anthropic/claude-3.5-sonnet:beta": "Anthropic: Claude 3.5 Sonnet (self-moderated)",
+ "x-ai/grok-beta": "xAI: Grok Beta",
+ "mistralai/ministral-8b": "Ministral 8B",
+ "mistralai/ministral-3b": "Ministral 3B",
+ "qwen/qwen-2.5-7b-instruct": "Qwen2.5 7B Instruct",
+ "nvidia/llama-3.1-nemotron-70b-instruct": "NVIDIA: Llama 3.1 Nemotron 70B Instruct",
+ "inflection/inflection-3-pi": "Inflection: Inflection 3 Pi",
+ "inflection/inflection-3-productivity": "Inflection: Inflection 3 Productivity",
+ "google/gemini-flash-1.5-8b": "Google: Gemini 1.5 Flash-8B",
+ "liquid/lfm-40b:free": "Liquid: LFM 40B MoE (free)",
+ "liquid/lfm-40b": "Liquid: LFM 40B MoE",
+ "thedrummer/rocinante-12b": "Rocinante 12B",
+ "eva-unit-01/eva-qwen-2.5-14b": "EVA Qwen2.5 14B",
+ "anthracite-org/magnum-v2-72b": "Magnum v2 72B",
+ "meta-llama/llama-3.2-3b-instruct:free": "Meta: Llama 3.2 3B Instruct (free)",
+ "meta-llama/llama-3.2-3b-instruct": "Meta: Llama 3.2 3B Instruct",
+ "meta-llama/llama-3.2-1b-instruct:free": "Meta: Llama 3.2 1B Instruct (free)",
+ "meta-llama/llama-3.2-1b-instruct": "Meta: Llama 3.2 1B Instruct",
+ "meta-llama/llama-3.2-90b-vision-instruct": "Meta: Llama 3.2 90B Vision Instruct",
+ "meta-llama/llama-3.2-11b-vision-instruct:free": "Meta: Llama 3.2 11B Vision Instruct (free)",
+ "meta-llama/llama-3.2-11b-vision-instruct": "Meta: Llama 3.2 11B Vision Instruct",
+ "qwen/qwen-2.5-72b-instruct": "Qwen2.5 72B Instruct",
+ "qwen/qwen-2-vl-72b-instruct": "Qwen2-VL 72B Instruct",
+ "neversleep/llama-3.1-lumimaid-8b": "Lumimaid v0.2 8B",
+ "openai/o1-mini-2024-09-12": "OpenAI: o1-mini (2024-09-12)",
+ "openai/o1-mini": "OpenAI: o1-mini",
+ "openai/o1-preview-2024-09-12": "OpenAI: o1-preview (2024-09-12)",
+ "openai/o1-preview": "OpenAI: o1-preview",
+ "mistralai/pixtral-12b": "Mistral: Pixtral 12B",
+ "cohere/command-r-plus-08-2024": "Cohere: Command R+ (08-2024)",
+ "cohere/command-r-08-2024": "Cohere: Command R (08-2024)",
+ "qwen/qwen-2-vl-7b-instruct": "Qwen2-VL 7B Instruct",
+ "google/gemini-flash-1.5-8b-exp": "Google: Gemini Flash 8B 1.5 Experimental",
+ "sao10k/l3.1-euryale-70b": "Llama 3.1 Euryale 70B v2.2",
+ "google/gemini-flash-1.5-exp": "Google: Gemini Flash 1.5 Experimental",
+ "ai21/jamba-1-5-large": "AI21: Jamba 1.5 Large",
+ "ai21/jamba-1-5-mini": "AI21: Jamba 1.5 Mini",
+ "microsoft/phi-3.5-mini-128k-instruct": "Phi-3.5 Mini 128K Instruct",
+ "nousresearch/hermes-3-llama-3.1-70b": "Nous: Hermes 3 70B Instruct",
+ "nousresearch/hermes-3-llama-3.1-405b:free": "Nous: Hermes 3 405B Instruct (free)",
+ "nousresearch/hermes-3-llama-3.1-405b": "Nous: Hermes 3 405B Instruct",
+ "nousresearch/hermes-3-llama-3.1-405b:extended": "Nous: Hermes 3 405B Instruct (extended)",
+ "perplexity/llama-3.1-sonar-huge-128k-online": "Perplexity: Llama 3.1 Sonar 405B Online",
+ "openai/chatgpt-4o-latest": "OpenAI: ChatGPT-4o",
+ "sao10k/l3-lunaris-8b": "Llama 3 8B Lunaris",
+ "aetherwiing/mn-starcannon-12b": "Mistral Nemo 12B Starcannon",
+ "openai/gpt-4o-2024-08-06": "OpenAI: GPT-4o (2024-08-06)",
+ "meta-llama/llama-3.1-405b": "Meta: Llama 3.1 405B (base)",
+ "nothingiisreal/mn-celeste-12b": "Mistral Nemo 12B Celeste",
+ "google/gemini-pro-1.5-exp": "Google: Gemini Pro 1.5 Experimental",
+ "perplexity/llama-3.1-sonar-large-128k-online": "Perplexity: Llama 3.1 Sonar 70B Online",
+ "perplexity/llama-3.1-sonar-large-128k-chat": "Perplexity: Llama 3.1 Sonar 70B",
+ "perplexity/llama-3.1-sonar-small-128k-online": "Perplexity: Llama 3.1 Sonar 8B Online",
+ "perplexity/llama-3.1-sonar-small-128k-chat": "Perplexity: Llama 3.1 Sonar 8B",
+ "meta-llama/llama-3.1-70b-instruct:free": "Meta: Llama 3.1 70B Instruct (free)",
"meta-llama/llama-3.1-70b-instruct": "Meta: Llama 3.1 70B Instruct",
+ "meta-llama/llama-3.1-70b-instruct:nitro": "Meta: Llama 3.1 70B Instruct (nitro)",
+ "meta-llama/llama-3.1-8b-instruct:free": "Meta: Llama 3.1 8B Instruct (free)",
"meta-llama/llama-3.1-8b-instruct": "Meta: Llama 3.1 8B Instruct",
+ "meta-llama/llama-3.1-405b-instruct:free": "Meta: Llama 3.1 405B Instruct (free)",
"meta-llama/llama-3.1-405b-instruct": "Meta: Llama 3.1 405B Instruct",
- "cognitivecomputations/dolphin-llama-3-70b": "Dolphin Llama 3 70B \ud83d\udc2c",
+ "meta-llama/llama-3.1-405b-instruct:nitro": "Meta: Llama 3.1 405B Instruct (nitro)",
"mistralai/codestral-mamba": "Mistral: Codestral Mamba",
"mistralai/mistral-nemo": "Mistral: Mistral Nemo",
"openai/gpt-4o-mini-2024-07-18": "OpenAI: GPT-4o-mini (2024-07-18)",
"openai/gpt-4o-mini": "OpenAI: GPT-4o-mini",
+ "qwen/qwen-2-7b-instruct:free": "Qwen 2 7B Instruct (free)",
"qwen/qwen-2-7b-instruct": "Qwen 2 7B Instruct",
"google/gemma-2-27b-it": "Google: Gemma 2 27B",
"alpindale/magnum-72b": "Magnum 72B",
"nousresearch/hermes-2-theta-llama-3-8b": "Nous: Hermes 2 Theta 8B",
"google/gemma-2-9b-it:free": "Google: Gemma 2 9B (free)",
"google/gemma-2-9b-it": "Google: Gemma 2 9B",
- "openrouter/flavor-of-the-week": "Flavor of The Week",
- "sao10k/l3-stheno-8b": "Llama 3 Stheno 8B v3.3 32K",
"ai21/jamba-instruct": "AI21: Jamba Instruct",
- "01-ai/yi-large": "Yi Large",
- "nvidia/nemotron-4-340b-instruct": "NVIDIA Nemotron-4 340B Instruct",
- "anthropic/claude-3.5-sonnet:beta": "Anthropic: Claude 3.5 Sonnet (self-moderated)",
- "anthropic/claude-3.5-sonnet": "Anthropic: Claude 3.5 Sonnet",
+ "anthropic/claude-3.5-sonnet-20240620": "Anthropic: Claude 3.5 Sonnet (2024-06-20)",
+ "anthropic/claude-3.5-sonnet-20240620:beta": "Anthropic: Claude 3.5 Sonnet (2024-06-20) (self-moderated)",
"sao10k/l3-euryale-70b": "Llama 3 Euryale 70B v2.1",
- "qwen/qwen-2-7b-instruct:free": "Qwen 2 7B Instruct (free)",
- "microsoft/phi-3-medium-4k-instruct": "Phi-3 Medium 4K Instruct",
"cognitivecomputations/dolphin-mixtral-8x22b": "Dolphin 2.9.2 Mixtral 8x22B \ud83d\udc2c",
"qwen/qwen-2-72b-instruct": "Qwen 2 72B Instruct",
- "openchat/openchat-8b": "OpenChat 3.6 8B",
"nousresearch/hermes-2-pro-llama-3-8b": "NousResearch: Hermes 2 Pro - Llama-3 8B",
"mistralai/mistral-7b-instruct-v0.3": "Mistral: Mistral 7B Instruct v0.3",
+ "mistralai/mistral-7b-instruct:free": "Mistral: Mistral 7B Instruct (free)",
"mistralai/mistral-7b-instruct": "Mistral: Mistral 7B Instruct",
+ "mistralai/mistral-7b-instruct:nitro": "Mistral: Mistral 7B Instruct (nitro)",
"microsoft/phi-3-mini-128k-instruct:free": "Phi-3 Mini 128K Instruct (free)",
"microsoft/phi-3-mini-128k-instruct": "Phi-3 Mini 128K Instruct",
"microsoft/phi-3-medium-128k-instruct:free": "Phi-3 Medium 128K Instruct (free)",
"microsoft/phi-3-medium-128k-instruct": "Phi-3 Medium 128K Instruct",
"neversleep/llama-3-lumimaid-70b": "Llama 3 Lumimaid 70B",
- "deepseek/deepseek-coder": "DeepSeek-Coder-V2",
- "deepseek/deepseek-chat": "DeepSeek-V2 Chat",
+ "google/gemini-flash-1.5": "Google: Gemini Flash 1.5",
+ "deepseek/deepseek-chat": "DeepSeek V2.5",
"perplexity/llama-3-sonar-large-32k-online": "Perplexity: Llama3 Sonar 70B Online",
"perplexity/llama-3-sonar-large-32k-chat": "Perplexity: Llama3 Sonar 70B",
- "perplexity/llama-3-sonar-small-32k-online": "Perplexity: Llama3 Sonar 8B Online",
"perplexity/llama-3-sonar-small-32k-chat": "Perplexity: Llama3 Sonar 8B",
- "google/gemini-flash-1.5": "Google: Gemini Flash 1.5",
"meta-llama/llama-guard-2-8b": "Meta: LlamaGuard 2 8B",
- "meta-llama/llama-3-70b": "Meta: Llama 3 70B (Base)",
- "meta-llama/llama-3-8b": "Meta: Llama 3 8B (Base)",
"openai/gpt-4o-2024-05-13": "OpenAI: GPT-4o (2024-05-13)",
"openai/gpt-4o": "OpenAI: GPT-4o",
- "allenai/olmo-7b-instruct": "OLMo 7B Instruct",
- "qwen/qwen-4b-chat": "Qwen 1.5 4B Chat",
- "qwen/qwen-7b-chat": "Qwen 1.5 7B Chat",
- "qwen/qwen-14b-chat": "Qwen 1.5 14B Chat",
- "qwen/qwen-32b-chat": "Qwen 1.5 32B Chat",
+ "openai/gpt-4o:extended": "OpenAI: GPT-4o (extended)",
"qwen/qwen-72b-chat": "Qwen 1.5 72B Chat",
"qwen/qwen-110b-chat": "Qwen 1.5 110B Chat",
- "meta-llama/llama-3-8b-instruct:free": "Meta: Llama 3 8B Instruct (free)",
- "neversleep/llama-3-lumimaid-8b:extended": "Llama 3 Lumimaid 8B (extended)",
"neversleep/llama-3-lumimaid-8b": "Llama 3 Lumimaid 8B",
- "snowflake/snowflake-arctic-instruct": "Snowflake: Arctic Instruct",
- "fireworks/firellava-13b": "FireLLaVA 13B",
- "lynn/soliloquy-l3": "Lynn: Llama 3 Soliloquy 8B v2",
- "meta-llama/llama-3-8b-instruct:extended": "Meta: Llama 3 8B Instruct (extended)",
+ "neversleep/llama-3-lumimaid-8b:extended": "Llama 3 Lumimaid 8B (extended)",
"sao10k/fimbulvetr-11b-v2": "Fimbulvetr 11B v2",
- "meta-llama/llama-3-70b-instruct:nitro": "Meta: Llama 3 70B Instruct (nitro)",
- "meta-llama/llama-3-8b-instruct:nitro": "Meta: Llama 3 8B Instruct (nitro)",
"meta-llama/llama-3-70b-instruct": "Meta: Llama 3 70B Instruct",
+ "meta-llama/llama-3-70b-instruct:nitro": "Meta: Llama 3 70B Instruct (nitro)",
+ "meta-llama/llama-3-8b-instruct:free": "Meta: Llama 3 8B Instruct (free)",
"meta-llama/llama-3-8b-instruct": "Meta: Llama 3 8B Instruct",
+ "meta-llama/llama-3-8b-instruct:nitro": "Meta: Llama 3 8B Instruct (nitro)",
+ "meta-llama/llama-3-8b-instruct:extended": "Meta: Llama 3 8B Instruct (extended)",
"mistralai/mixtral-8x22b-instruct": "Mistral: Mixtral 8x22B Instruct",
"microsoft/wizardlm-2-7b": "WizardLM-2 7B",
"microsoft/wizardlm-2-8x22b": "WizardLM-2 8x22B",
- "undi95/toppy-m-7b:nitro": "Toppy M 7B (nitro)",
- "mistralai/mixtral-8x22b": "Mistral: Mixtral 8x22B (base)",
"google/gemini-pro-1.5": "Google: Gemini Pro 1.5",
"openai/gpt-4-turbo": "OpenAI: GPT-4 Turbo",
"cohere/command-r-plus": "Cohere: Command R+",
+ "cohere/command-r-plus-04-2024": "Cohere: Command R+ (04-2024)",
"databricks/dbrx-instruct": "Databricks: DBRX 132B Instruct",
"sophosympatheia/midnight-rose-70b": "Midnight Rose 70B",
"cohere/command-r": "Cohere: Command R",
"cohere/command": "Cohere: Command",
- "anthropic/claude-3-haiku:beta": "Anthropic: Claude 3 Haiku (self-moderated)",
"anthropic/claude-3-haiku": "Anthropic: Claude 3 Haiku",
- "google/gemma-7b-it:nitro": "Google: Gemma 7B (nitro)",
- "mistralai/mistral-7b-instruct:nitro": "Mistral: Mistral 7B Instruct (nitro)",
- "mistralai/mixtral-8x7b-instruct:nitro": "Mixtral 8x7B Instruct (nitro)",
- "gryphe/mythomax-l2-13b:nitro": "MythoMax 13B (nitro)",
- "anthropic/claude-3-sonnet:beta": "Anthropic: Claude 3 Sonnet (self-moderated)",
- "anthropic/claude-3-opus:beta": "Anthropic: Claude 3 Opus (self-moderated)",
+ "anthropic/claude-3-haiku:beta": "Anthropic: Claude 3 Haiku (self-moderated)",
"anthropic/claude-3-sonnet": "Anthropic: Claude 3 Sonnet",
+ "anthropic/claude-3-sonnet:beta": "Anthropic: Claude 3 Sonnet (self-moderated)",
"anthropic/claude-3-opus": "Anthropic: Claude 3 Opus",
+ "anthropic/claude-3-opus:beta": "Anthropic: Claude 3 Opus (self-moderated)",
+ "cohere/command-r-03-2024": "Cohere: Command R (03-2024)",
"mistralai/mistral-large": "Mistral Large",
- "google/gemma-7b-it:free": "Google: Gemma 7B (free)",
- "google/gemma-7b-it": "Google: Gemma 7B",
- "nousresearch/nous-hermes-2-mistral-7b-dpo": "Nous: Hermes 2 Mistral 7B DPO",
- "meta-llama/codellama-70b-instruct": "Meta: CodeLlama 70B Instruct",
- "recursal/eagle-7b": "RWKV v5: Eagle 7B",
"openai/gpt-4-turbo-preview": "OpenAI: GPT-4 Turbo Preview",
"openai/gpt-3.5-turbo-0613": "OpenAI: GPT-3.5 Turbo (older v0613)",
- "undi95/remm-slerp-l2-13b:extended": "ReMM SLERP 13B (extended)",
- "nousresearch/nous-hermes-2-mixtral-8x7b-sft": "Nous: Hermes 2 Mixtral 8x7B SFT",
"nousresearch/nous-hermes-2-mixtral-8x7b-dpo": "Nous: Hermes 2 Mixtral 8x7B DPO",
"mistralai/mistral-medium": "Mistral Medium",
"mistralai/mistral-small": "Mistral Small",
"mistralai/mistral-tiny": "Mistral Tiny",
- "austism/chronos-hermes-13b": "Chronos Hermes 13B v2",
- "nousresearch/nous-hermes-yi-34b": "Nous: Hermes 2 Yi 34B",
"mistralai/mistral-7b-instruct-v0.2": "Mistral: Mistral 7B Instruct v0.2",
"cognitivecomputations/dolphin-mixtral-8x7b": "Dolphin 2.6 Mixtral 8x7B \ud83d\udc2c",
- "google/gemini-pro-vision": "Google: Gemini Pro Vision 1.0",
"google/gemini-pro": "Google: Gemini Pro 1.0",
- "recursal/rwkv-5-3b-ai-town": "RWKV v5 3B AI Town",
- "rwkv/rwkv-5-world-3b": "RWKV v5 World 3B",
+ "google/gemini-pro-vision": "Google: Gemini Pro Vision 1.0",
"mistralai/mixtral-8x7b-instruct": "Mixtral 8x7B Instruct",
+ "mistralai/mixtral-8x7b-instruct:nitro": "Mixtral 8x7B Instruct (nitro)",
"mistralai/mixtral-8x7b": "Mixtral 8x7B (base)",
- "togethercomputer/stripedhyena-hessian-7b": "StripedHyena Hessian 7B (base)",
- "togethercomputer/stripedhyena-nous-7b": "StripedHyena Nous 7B",
- "koboldai/psyfighter-13b-2": "Psyfighter v2 13B",
+ "gryphe/mythomist-7b:free": "MythoMist 7B (free)",
"gryphe/mythomist-7b": "MythoMist 7B",
- "01-ai/yi-6b": "Yi 6B (base)",
- "01-ai/yi-34b": "Yi 34B (base)",
- "01-ai/yi-34b-chat": "Yi 34B Chat",
- "nousresearch/nous-capybara-7b:free": "Nous: Capybara 7B (free)",
- "nousresearch/nous-capybara-7b": "Nous: Capybara 7B",
"openchat/openchat-7b:free": "OpenChat 3.5 7B (free)",
"openchat/openchat-7b": "OpenChat 3.5 7B",
- "gryphe/mythomist-7b:free": "MythoMist 7B (free)",
"neversleep/noromaid-20b": "Noromaid 20B",
- "intel/neural-chat-7b": "Neural Chat 7B v3.1",
- "anthropic/claude-2.1:beta": "Anthropic: Claude v2.1 (self-moderated)",
- "anthropic/claude-2:beta": "Anthropic: Claude v2 (self-moderated)",
"anthropic/claude-instant-1.1": "Anthropic: Claude Instant v1.1",
"anthropic/claude-2.1": "Anthropic: Claude v2.1",
+ "anthropic/claude-2.1:beta": "Anthropic: Claude v2.1 (self-moderated)",
"anthropic/claude-2": "Anthropic: Claude v2",
+ "anthropic/claude-2:beta": "Anthropic: Claude v2 (self-moderated)",
"teknium/openhermes-2.5-mistral-7b": "OpenHermes 2.5 Mistral 7B",
"openai/gpt-4-vision-preview": "OpenAI: GPT-4 Vision",
"lizpreciatior/lzlv-70b-fp16-hf": "lzlv 70B",
- "undi95/toppy-m-7b:free": "Toppy M 7B (free)",
"alpindale/goliath-120b": "Goliath 120B",
+ "undi95/toppy-m-7b:free": "Toppy M 7B (free)",
"undi95/toppy-m-7b": "Toppy M 7B",
+ "undi95/toppy-m-7b:nitro": "Toppy M 7B (nitro)",
"openrouter/auto": "Auto (best for prompt)",
"openai/gpt-4-1106-preview": "OpenAI: GPT-4 Turbo (older v1106)",
"openai/gpt-3.5-turbo-1106": "OpenAI: GPT-3.5 Turbo 16k (older v1106)",
- "huggingfaceh4/zephyr-7b-beta:free": "Hugging Face: Zephyr 7B (free)",
"google/palm-2-codechat-bison-32k": "Google: PaLM 2 Code Chat 32k",
"google/palm-2-chat-bison-32k": "Google: PaLM 2 Chat 32k",
- "teknium/openhermes-2-mistral-7b": "OpenHermes 2 Mistral 7B",
- "open-orca/mistral-7b-openorca": "Mistral OpenOrca 7B",
"jondurbin/airoboros-l2-70b": "Airoboros 70B",
- "gryphe/mythomax-l2-13b:extended": "MythoMax 13B (extended)",
"xwin-lm/xwin-lm-70b": "Xwin 70B",
- "mistralai/mistral-7b-instruct:free": "Mistral: Mistral 7B Instruct (free)",
"mistralai/mistral-7b-instruct-v0.1": "Mistral: Mistral 7B Instruct v0.1",
"openai/gpt-3.5-turbo-instruct": "OpenAI: GPT-3.5 Turbo Instruct",
"pygmalionai/mythalion-13b": "Pygmalion: Mythalion 13B",
@@ -461,25 +500,26 @@ fetchDefaultModel();
"openai/gpt-4-32k": "OpenAI: GPT-4 32k",
"openai/gpt-3.5-turbo-16k": "OpenAI: GPT-3.5 Turbo 16k",
"nousresearch/nous-hermes-llama2-13b": "Nous: Hermes 13B",
- "phind/phind-codellama-34b": "Phind: CodeLlama 34B v2",
- "meta-llama/codellama-34b-instruct": "Meta: CodeLlama 34B Instruct",
+ "huggingfaceh4/zephyr-7b-beta:free": "Hugging Face: Zephyr 7B (free)",
"mancer/weaver": "Mancer: Weaver (alpha)",
- "anthropic/claude-instant-1:beta": "Anthropic: Claude Instant v1 (self-moderated)",
- "anthropic/claude-2.0:beta": "Anthropic: Claude v2.0 (self-moderated)",
"anthropic/claude-instant-1.0": "Anthropic: Claude Instant v1.0",
"anthropic/claude-1.2": "Anthropic: Claude v1.2",
"anthropic/claude-1": "Anthropic: Claude v1",
"anthropic/claude-instant-1": "Anthropic: Claude Instant v1",
+ "anthropic/claude-instant-1:beta": "Anthropic: Claude Instant v1 (self-moderated)",
"anthropic/claude-2.0": "Anthropic: Claude v2.0",
+ "anthropic/claude-2.0:beta": "Anthropic: Claude v2.0 (self-moderated)",
"undi95/remm-slerp-l2-13b": "ReMM SLERP 13B",
+ "undi95/remm-slerp-l2-13b:extended": "ReMM SLERP 13B (extended)",
"google/palm-2-codechat-bison": "Google: PaLM 2 Code Chat",
"google/palm-2-chat-bison": "Google: PaLM 2 Chat",
+ "gryphe/mythomax-l2-13b:free": "MythoMax 13B (free)",
"gryphe/mythomax-l2-13b": "MythoMax 13B",
- "meta-llama/llama-2-70b-chat": "Meta: Llama v2 70B Chat",
+ "gryphe/mythomax-l2-13b:nitro": "MythoMax 13B (nitro)",
+ "gryphe/mythomax-l2-13b:extended": "MythoMax 13B (extended)",
"meta-llama/llama-2-13b-chat": "Meta: Llama v2 13B Chat",
"openai/gpt-4-0314": "OpenAI: GPT-4 (older v0314)",
"openai/gpt-4": "OpenAI: GPT-4",
- "openai/gpt-3.5-turbo-0301": "OpenAI: GPT-3.5 Turbo (older v0301)",
"openai/gpt-3.5-turbo-0125": "OpenAI: GPT-3.5 Turbo 16k",
"openai/gpt-3.5-turbo": "OpenAI: GPT-3.5 Turbo"
};
@@ -670,155 +710,174 @@ const selectedModelDisplayName = document.getElementById('selected-model').textC
"gpt-4o-mini": "GPT-4o-Mini: Small, fast, and cheap model from OpenAI with relatively high intelligence.",
"mixtral-8x7b-32768": "Qroq API: Free Mixtral 8x7b",
// open router models
- "meta-llama/llama-3.1-70b-instruct": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3.1-8b-instruct": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3.1-405b-instruct": "The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "cognitivecomputations/dolphin-llama-3-70b": "Dolphin 2.9 is designed for instruction following, conversational, and coding. This model is a fine-tune of [Llama 3 70B](/models/meta-llama/llama-3-70b-instruct). It demonstrates improvements in instruction, conversation, coding, and function calling abilities, when compared to the original.\n\nUncensored and is stripped of alignment and bias, it requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
+ "neversleep/llama-3.1-lumimaid-70b": "Lumimaid v0.2 70B is a finetune of [Llama 3.1 70B](/meta-llama/llama-3.1-70b-instruct) with a \"HUGE step up dataset wise\" compared to Lumimaid v0.1. Sloppy chats output were purged.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
+ "anthracite-org/magnum-v4-72b": "This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus).\n\nThe model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-2.5-72b-instruct).",
+ "anthropic/claude-3.5-sonnet": "The new Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: New Sonnet scores ~49% on SWE-Bench Verified, higher than the last best score, and without any fancy prompt scaffolding\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal",
+ "anthropic/claude-3.5-sonnet:beta": "The new Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: New Sonnet scores ~49% on SWE-Bench Verified, higher than the last best score, and without any fancy prompt scaffolding\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-3.5-sonnet) variant._",
+ "x-ai/grok-beta": "Grok Beta is xAI's experimental language model with state-of-the-art reasoning capabilities, best for complex and multi-step use cases.\n\nIt is the successor of [Grok 2](https://x.ai/blog/grok-2) with enhanced context length.",
+ "mistralai/ministral-8b": "Ministral 8B is an 8B parameter model featuring a unique interleaved sliding-window attention pattern for faster, memory-efficient inference. Designed for edge use cases, it supports up to 128k context length and excels in knowledge and reasoning tasks. It outperforms peers in the sub-10B category, making it perfect for low-latency, privacy-first applications.",
+ "mistralai/ministral-3b": "Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it\u2019s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.",
+ "qwen/qwen-2.5-7b-instruct": "Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
+ "nvidia/llama-3.1-nemotron-70b-instruct": "NVIDIA's Llama 3.1 Nemotron 70B is a language model designed for generating precise and useful responses. Leveraging [Llama 3.1 70B](/models/meta-llama/llama-3.1-70b-instruct) architecture and Reinforcement Learning from Human Feedback (RLHF), it excels in automatic alignment benchmarks. This model is tailored for applications requiring high accuracy in helpfulness and response generation, suitable for diverse user queries across multiple domains.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "inflection/inflection-3-pi": "Inflection 3 Pi powers Inflection's [Pi](https://pi.ai) chatbot, including backstory, emotional intelligence, productivity, and safety. It has access to recent news, and excels in scenarios like customer support and roleplay.\n\nPi has been trained to mirror your tone and style, if you use more emojis, so will Pi! Try experimenting with various prompts and conversation styles.",
+ "inflection/inflection-3-productivity": "Inflection 3 Productivity is optimized for following instructions. It is better for tasks requiring JSON output or precise adherence to provided guidelines. It has access to recent news.\n\nFor emotional intelligence similar to Pi, see [Inflect 3 Pi](/inflection/inflection-3-pi)\n\nSee [Inflection's announcement](https://inflection.ai/blog/enterprise) for more details.",
+ "google/gemini-flash-1.5-8b": "Gemini 1.5 Flash-8B is optimized for speed and efficiency, offering enhanced performance in small prompt tasks like chat, transcription, and translation. With reduced latency, it is highly effective for real-time and large-scale operations. This model focuses on cost-effective solutions while maintaining high-quality results.\n\n[Click here to learn more about this model](https://developers.googleblog.com/en/gemini-15-flash-8b-is-now-generally-available-for-use/).\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).",
+ "liquid/lfm-40b:free": "Liquid's 40.3B Mixture of Experts (MoE) model. Liquid Foundation Models (LFMs) are large neural networks built with computational units rooted in dynamic systems.\n\nLFMs are general-purpose AI models that can be used to model any kind of sequential data, including video, audio, text, time series, and signals.\n\nSee the [launch announcement](https://www.liquid.ai/liquid-foundation-models) for benchmarks and more info.\n\n_These are free, rate-limited endpoints for [LFM 40B MoE](/liquid/lfm-40b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "liquid/lfm-40b": "Liquid's 40.3B Mixture of Experts (MoE) model. Liquid Foundation Models (LFMs) are large neural networks built with computational units rooted in dynamic systems.\n\nLFMs are general-purpose AI models that can be used to model any kind of sequential data, including video, audio, text, time series, and signals.\n\nSee the [launch announcement](https://www.liquid.ai/liquid-foundation-models) for benchmarks and more info.",
+ "thedrummer/rocinante-12b": "Rocinante 12B is designed for engaging storytelling and rich prose.\n\nEarly testers have reported:\n- Expanded vocabulary with unique and expressive word choices\n- Enhanced creativity for vivid narratives\n- Adventure-filled and captivating stories",
+ "eva-unit-01/eva-qwen-2.5-14b": "A model specializing in RP and creative writing, this model is based on Qwen2.5-14B, fine-tuned with a mixture of synthetic and natural data.\n\nIt is trained on 1.5M tokens of role-play data, and fine-tuned on 1.5M tokens of synthetic data.",
+ "anthracite-org/magnum-v2-72b": "From the maker of [Goliath](https://openrouter.ai/alpindale/goliath-120b), Magnum 72B is the seventh in a family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.",
+ "meta-llama/llama-3.2-3b-instruct:free": "Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.2 3B Instruct](/meta-llama/llama-3.2-3b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.2-3b-instruct": "Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it supports eight languages, including English, Spanish, and Hindi, and is adaptable for additional languages.\n\nTrained on 9 trillion tokens, the Llama 3.2B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.2-1b-instruct:free": "Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance.\n\nSupporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.2 1B Instruct](/meta-llama/llama-3.2-1b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.2-1b-instruct": "Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance.\n\nSupporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.2-90b-vision-instruct": "The Llama 90B Vision model is a top-tier, 90-billion-parameter multimodal model designed for the most challenging visual reasoning and language tasks. It offers unparalleled accuracy in image captioning, visual question answering, and advanced image-text comprehension. Pre-trained on vast multimodal datasets and fine-tuned with human feedback, the Llama 90B Vision is engineered to handle the most demanding image-based AI tasks.\n\nThis model is perfect for industries requiring cutting-edge multimodal AI capabilities, particularly those dealing with complex, real-time visual and textual analysis.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.2-11b-vision-instruct:free": "Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.\n\nIts ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.2 11B Vision Instruct](/meta-llama/llama-3.2-11b-vision-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.2-11b-vision-instruct": "Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.\n\nIts ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.\n\nClick here for the [original model card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD_VISION.md).\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "qwen/qwen-2.5-72b-instruct": "Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
+ "qwen/qwen-2-vl-72b-instruct": "Qwen2 VL 72B is a multimodal LLM from the Qwen Team with the following key enhancements:\n\n- SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.\n\n- Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.\n\n- Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.\n\n- Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub repo](https://github.com/QwenLM/Qwen2-VL).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
+ "neversleep/llama-3.1-lumimaid-8b": "Lumimaid v0.2 8B is a finetune of [Llama 3.1 8B](/meta-llama/llama-3.1-8b-instruct) with a \"HUGE step up dataset wise\" compared to Lumimaid v0.1. Sloppy chats output were purged.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
+ "openai/o1-mini-2024-09-12": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "openai/o1-mini": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "openai/o1-preview-2024-09-12": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "openai/o1-preview": "The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding.\n\nThe o1 models are optimized for math, science, programming, and other STEM-related tasks. They consistently exhibit PhD-level accuracy on benchmarks in physics, chemistry, and biology. Learn more in the [launch announcement](https://openai.com/o1).\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "mistralai/pixtral-12b": "The first image to text model from Mistral AI. Its weight was launched via torrent per their tradition: https://x.com/mistralai/status/1833758285167722836",
+ "cohere/command-r-plus-08-2024": "command-r-plus-08-2024 is an update of the [Command R+](/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint the same.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
+ "cohere/command-r-08-2024": "command-r-08-2024 is an update of the [Command R](/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and is competitive with the previous version of the larger Command R+ model.\n\nRead the launch post [here](https://docs.cohere.com/changelog/command-gets-refreshed).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
+ "qwen/qwen-2-vl-7b-instruct": "Qwen2 VL 7B is a multimodal LLM from the Qwen Team with the following key enhancements:\n\n- SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.\n\n- Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.\n\n- Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.\n\n- Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub repo](https://github.com/QwenLM/Qwen2-VL).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
+ "google/gemini-flash-1.5-8b-exp": "Gemini 1.5 Flash 8B Experimental is an experimental, 8B parameter version of the [Gemini 1.5 Flash](/google/gemini-flash-1.5) model.\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "sao10k/l3.1-euryale-70b": "Euryale L3.1 70B v2.2 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.1](/sao10k/l3-euryale-70b).",
+ "google/gemini-flash-1.5-exp": "Gemini 1.5 Flash Experimental is an experimental version of the [Gemini 1.5 Flash](/google/gemini-flash-1.5) model.\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "ai21/jamba-1-5-large": "Jamba 1.5 Large is part of AI21's new family of open models, offering superior speed, efficiency, and quality.\n\nIt features a 256K effective context window, the longest among open models, enabling improved performance on tasks like document summarization and analysis.\n\nBuilt on a novel SSM-Transformer architecture, it outperforms larger models like Llama 3.1 70B on benchmarks while maintaining resource efficiency.\n\nRead their [announcement](https://www.ai21.com/blog/announcing-jamba-model-family) to learn more.",
+ "ai21/jamba-1-5-mini": "Jamba 1.5 Mini is the world's first production-grade Mamba-based model, combining SSM and Transformer architectures for a 256K context window and high efficiency.\n\nIt works with 9 languages and can handle various writing and analysis tasks as well as or better than similar small models.\n\nThis model uses less computer memory and works faster with longer texts than previous designs.\n\nRead their [announcement](https://www.ai21.com/blog/announcing-jamba-model-family) to learn more.",
+ "microsoft/phi-3.5-mini-128k-instruct": "Phi-3.5 models are lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets that include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. Phi-3.5 Mini uses 3.8B parameters, and is a dense decoder-only transformer model using the same tokenizer as [Phi-3 Mini](/microsoft/phi-3-mini-128k-instruct).\n\nThe models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures. When assessed against benchmarks that test common sense, language understanding, math, code, long context and logical reasoning, Phi-3.5 models showcased robust and state-of-the-art performance among models with less than 13 billion parameters.",
+ "nousresearch/hermes-3-llama-3.1-70b": "Hermes 3 is a generalist language model with many improvements over [Hermes 2](/nousresearch/nous-hermes-2-mistral-7b-dpo), including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 70B is a competitive, if not superior finetune of the [Llama-3.1 70B foundation model](/meta-llama/llama-3.1-70b-instruct), focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.",
+ "nousresearch/hermes-3-llama-3.1-405b:free": "Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 405B is a frontier-level, full-parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.\n\nHermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.\n\n_These are free, rate-limited endpoints for [Hermes 3 405B Instruct](/nousresearch/hermes-3-llama-3.1-405b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "nousresearch/hermes-3-llama-3.1-405b": "Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 405B is a frontier-level, full-parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.\n\nHermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.",
+ "nousresearch/hermes-3-llama-3.1-405b:extended": "Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the board.\n\nHermes 3 405B is a frontier-level, full-parameter finetune of the Llama-3.1 405B foundation model, focused on aligning LLMs to the user, with powerful steering capabilities and control given to the end user.\n\nThe Hermes 3 series builds and expands on the Hermes 2 set of capabilities, including more powerful and reliable function calling and structured output capabilities, generalist assistant capabilities, and improved code generation skills.\n\nHermes 3 is competitive, if not superior, to Llama-3.1 Instruct models at general capabilities, with varying strengths and weaknesses attributable between the two.\n\n_These are extended-context endpoints for [Hermes 3 405B Instruct](/nousresearch/hermes-3-llama-3.1-405b). They may have higher prices._",
+ "perplexity/llama-3.1-sonar-huge-128k-online": "Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance. The model is built upon the Llama 3.1 405B and has internet access.",
+ "openai/chatgpt-4o-latest": "Dynamic model continuously updated to the current version of [GPT-4o](/openai/gpt-4o) in ChatGPT. Intended for research and evaluation.\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "sao10k/l3-lunaris-8b": "Lunaris 8B is a versatile generalist and roleplaying model based on Llama 3. It's a strategic merge of multiple models, designed to balance creativity with improved logic and general knowledge.\n\nCreated by [Sao10k](https://huggingface.co/Sao10k), this model aims to offer an improved experience over Stheno v3.2, with enhanced creativity and logical reasoning.\n\nFor best results, use with Llama 3 Instruct context template, temperature 1.4, and min_p 0.1.",
+ "aetherwiing/mn-starcannon-12b": "Starcannon 12B is a creative roleplay and story writing model, using [nothingiisreal/mn-celeste-12b](https://openrouter.ai/nothingiisreal/mn-celeste-12b) as a base and [intervitens/mini-magnum-12b-v1.1](https://huggingface.co/intervitens/mini-magnum-12b-v1.1) merged in using the [TIES](https://arxiv.org/abs/2306.01708) method.\n\nAlthough more similar to Magnum overall, the model remains very creative, with a pleasant writing style. It is recommended for people wanting more variety than Magnum, and yet more verbose prose than Celeste.",
+ "openai/gpt-4o-2024-08-06": "The 2024-08-06 version of GPT-4o offers improved performance in structured outputs, with the ability to supply a JSON schema in the respone_format. Read more [here](https://openai.com/index/introducing-structured-outputs-in-the-api/).\n\nGPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)",
+ "meta-llama/llama-3.1-405b": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This is the base 405B pre-trained version.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "nothingiisreal/mn-celeste-12b": "A specialized story writing and roleplaying model based on Mistral's NeMo 12B Instruct. Fine-tuned on curated datasets including Reddit Writing Prompts and Opus Instruct 25K.\n\nThis model excels at creative writing, offering improved NSFW capabilities, with smarter and more active narration. It demonstrates remarkable versatility in both SFW and NSFW scenarios, with strong Out of Character (OOC) steering capabilities, allowing fine-tuned control over narrative direction and character behavior.\n\nCheck out the model's [HuggingFace page](https://huggingface.co/nothingiisreal/MN-12B-Celeste-V1.9) for details on what parameters and prompts work best!",
+ "google/gemini-pro-1.5-exp": "Gemini 1.5 Pro (0827) is an experimental version of the [Gemini 1.5 Pro](/google/gemini-pro-1.5) model.\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal\n\nNote: This model is currently experimental and not suitable for production use-cases, and may be heavily rate-limited.",
+ "perplexity/llama-3.1-sonar-large-128k-online": "Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is the online version of the [offline chat model](/perplexity/llama-3.1-sonar-large-128k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online",
+ "perplexity/llama-3.1-sonar-large-128k-chat": "Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/perplexity/llama-3.1-sonar-large-128k-online) of this model has Internet access.",
+ "perplexity/llama-3.1-sonar-small-128k-online": "Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is the online version of the [offline chat model](/perplexity/llama-3.1-sonar-small-128k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online",
+ "perplexity/llama-3.1-sonar-small-128k-chat": "Llama 3.1 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/perplexity/llama-3.1-sonar-small-128k-online) of this model has Internet access.",
+ "meta-llama/llama-3.1-70b-instruct:free": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.1 70B Instruct](/meta-llama/llama-3.1-70b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.1-70b-instruct": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.1-70b-instruct:nitro": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are higher-throughput endpoints for [Llama 3.1 70B Instruct](/meta-llama/llama-3.1-70b-instruct). They may have higher prices._",
+ "meta-llama/llama-3.1-8b-instruct:free": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.1 8B Instruct](/meta-llama/llama-3.1-8b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.1-8b-instruct": "Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.1-405b-instruct:free": "The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3.1 405B Instruct](/meta-llama/llama-3.1-405b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3.1-405b-instruct": "The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3.1-405b-instruct:nitro": "The highly anticipated 400B class of Llama3 is here! Clocking in at 128k context with impressive eval scores, the Meta AI team continues to push the frontier of open-source LLMs.\n\nMeta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 405B instruct-tuned version is optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are higher-throughput endpoints for [Llama 3.1 405B Instruct](/meta-llama/llama-3.1-405b-instruct). They may have higher prices._",
"mistralai/codestral-mamba": "A 7.3B parameter Mamba-based model designed for code and reasoning tasks.\n\n- Linear time inference, allowing for theoretically infinite sequence lengths\n- 256k token context window\n- Optimized for quick responses, especially beneficial for code productivity\n- Performs comparably to state-of-the-art transformer models in code and reasoning tasks\n- Available under the Apache 2.0 license for free use, modification, and distribution",
"mistralai/mistral-nemo": "A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA.\n\nThe model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi.\n\nIt supports function calling and is released under the Apache 2.0 license.",
- "openai/gpt-4o-mini-2024-07-18": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal",
- "openai/gpt-4o-mini": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.\n\n#multimodal",
+ "openai/gpt-4o-mini-2024-07-18": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.",
+ "openai/gpt-4o-mini": "GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/openai/gpt-4o), supporting both text and image inputs with text outputs.\n\nAs their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective.\n\nGPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences [common leaderboards](https://arena.lmsys.org/).\n\nCheck out the [launch announcement](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/) to learn more.",
+ "qwen/qwen-2-7b-instruct:free": "Qwen2 7B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).\n\n_These are free, rate-limited endpoints for [Qwen 2 7B Instruct](/qwen/qwen-2-7b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"qwen/qwen-2-7b-instruct": "Qwen2 7B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
"google/gemma-2-27b-it": "Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini).\n\nGemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).",
- "alpindale/magnum-72b": "From the maker of [Goliath](https://openrouter.ai/models/alpindale/goliath-120b), Magnum 72B is the first in a new family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/models/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.",
- "nousresearch/hermes-2-theta-llama-3-8b": "An experimental merge model based on Llama 3, exhibiting a very distinctive style of writing. It combines the the best of [Meta's Llama 3 8B](https://openrouter.ai/models/meta-llama/llama-3-8b-instruct) and Nous Research's [Hermes 2 Pro](https://openrouter.ai/models/nousresearch/hermes-2-pro-llama-3-8b).\n\nHermes-2 \u0398 (theta) was specifically designed with a few capabilities in mind: executing function calls, generating JSON output, and most remarkably, demonstrating metacognitive abilities (contemplating the nature of thought and recognizing the diversity of cognitive processes among individuals).",
- "google/gemma-2-9b-it:free": "Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.\n\nDesigned for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).\n\nNote: this is a free, rate-limited version of [Gemma 2 9B](/models/google/gemma-2-9b-it). Outputs may be cached. Read about rate limits [here](/docs/limits).",
+ "alpindale/magnum-72b": "From the maker of [Goliath](https://openrouter.ai/alpindale/goliath-120b), Magnum 72B is the first in a new family of models designed to achieve the prose quality of the Claude 3 models, notably Opus & Sonnet.\n\nThe model is based on [Qwen2 72B](https://openrouter.ai/qwen/qwen-2-72b-instruct) and trained with 55 million tokens of highly curated roleplay (RP) data.",
+ "nousresearch/hermes-2-theta-llama-3-8b": "An experimental merge model based on Llama 3, exhibiting a very distinctive style of writing. It combines the the best of [Meta's Llama 3 8B](https://openrouter.ai/meta-llama/llama-3-8b-instruct) and Nous Research's [Hermes 2 Pro](https://openrouter.ai/nousresearch/hermes-2-pro-llama-3-8b).\n\nHermes-2 \u0398 (theta) was specifically designed with a few capabilities in mind: executing function calls, generating JSON output, and most remarkably, demonstrating metacognitive abilities (contemplating the nature of thought and recognizing the diversity of cognitive processes among individuals).",
+ "google/gemma-2-9b-it:free": "Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.\n\nDesigned for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).\n\n_These are free, rate-limited endpoints for [Gemma 2 9B](/google/gemma-2-9b-it). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"google/gemma-2-9b-it": "Gemma 2 9B by Google is an advanced, open-source language model that sets a new standard for efficiency and performance in its size class.\n\nDesigned for a wide variety of tasks, it empowers developers and researchers to build innovative applications, while maintaining accessibility, safety, and cost-effectiveness.\n\nSee the [launch announcement](https://blog.google/technology/developers/google-gemma-2/) for more details. Usage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).",
- "openrouter/flavor-of-the-week": "This is a router model that rotates its underlying model weekly. It aims to be a simple way to explore the capabilities of new models while using the same model ID.\n\nThe current underlying model is [Llama 3 Stheno 8B v3.3 32K](/models/sao10k/l3-stheno-8b).\n\nNOTE: Pricing depends on the underlying model as well as the provider routed to. To see which model and provider were used, visit [Activity](/activity).",
- "sao10k/l3-stheno-8b": "Stheno 8B 32K is a creative writing/roleplay model from [Sao10k](https://ko-fi.com/sao10k). It was trained at 8K context, then expanded to 32K context.\n\nCompared to older Stheno version, this model is trained on:\n- 2x the amount of creative writing samples\n- Cleaned up roleplaying samples\n- Fewer low quality samples",
"ai21/jamba-instruct": "The Jamba-Instruct model, introduced by AI21 Labs, is an instruction-tuned variant of their hybrid SSM-Transformer Jamba model, specifically optimized for enterprise applications.\n\n- 256K Context Window: It can process extensive information, equivalent to a 400-page novel, which is beneficial for tasks involving large documents such as financial reports or legal documents\n- Safety and Accuracy: Jamba-Instruct is designed with enhanced safety features to ensure secure deployment in enterprise environments, reducing the risk and cost of implementation\n\nRead their [announcement](https://www.ai21.com/blog/announcing-jamba) to learn more.\n\nJamba has a knowledge cutoff of February 2024.",
- "01-ai/yi-large": "The Yi Large model was designed by 01.AI with the following usecases in mind: knowledge search, data classification, human-like chat bots, and customer service.\n\nIt stands out for its multilingual proficiency, particularly in Spanish, Chinese, Japanese, German, and French.\n\nCheck out the [launch announcement](https://01-ai.github.io/blog/01.ai-yi-large-llm-launch) to learn more.",
- "nvidia/nemotron-4-340b-instruct": "Nemotron-4-340B-Instruct is an English-language chat model optimized for synthetic data generation. This large language model (LLM) is a fine-tuned version of Nemotron-4-340B-Base, designed for single and multi-turn chat use-cases with a 4,096 token context length.\n\nThe base model was pre-trained on 9 trillion tokens from diverse English texts, 50+ natural languages, and 40+ coding languages. The instruct model underwent additional alignment steps:\n\n1. Supervised Fine-tuning (SFT)\n2. Direct Preference Optimization (DPO)\n3. Reward-aware Preference Optimization (RPO)\n\nThe alignment process used approximately 20K human-annotated samples, while 98% of the data for fine-tuning was synthetically generated. Detailed information about the synthetic data generation pipeline is available in the [technical report](https://arxiv.org/html/2406.11704v1).",
- "anthropic/claude-3.5-sonnet:beta": "This is a lower-latency version of [Claude 3.5 Sonnet](/models/anthropic/claude-3.5-sonnet), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Autonomously writes, edits, and runs code with reasoning and troubleshooting\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal",
- "anthropic/claude-3.5-sonnet": "Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Autonomously writes, edits, and runs code with reasoning and troubleshooting\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\n#multimodal",
+ "anthropic/claude-3.5-sonnet-20240620": "Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Autonomously writes, edits, and runs code with reasoning and troubleshooting\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\nFor the latest version (2024-10-23), check out [Claude 3.5 Sonnet](/anthropic/claude-3.5-sonnet).\n\n#multimodal",
+ "anthropic/claude-3.5-sonnet-20240620:beta": "Claude 3.5 Sonnet delivers better-than-Opus capabilities, faster-than-Sonnet speeds, at the same Sonnet prices. Sonnet is particularly good at:\n\n- Coding: Autonomously writes, edits, and runs code with reasoning and troubleshooting\n- Data science: Augments human data science expertise; navigates unstructured data while using multiple tools for insights\n- Visual processing: excelling at interpreting charts, graphs, and images, accurately transcribing text to derive insights beyond just the text alone\n- Agentic tasks: exceptional tool use, making it great at agentic tasks (i.e. complex, multi-step problem solving tasks that require engaging with other systems)\n\nFor the latest version (2024-10-23), check out [Claude 3.5 Sonnet](/anthropic/claude-3.5-sonnet).\n\n#multimodal\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-3.5-sonnet-20240620) variant._",
"sao10k/l3-euryale-70b": "Euryale 70B v2.1 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k).\n\n- Better prompt adherence.\n- Better anatomy / spatial awareness.\n- Adapts much better to unique and custom formatting / reply formats.\n- Very creative, lots of unique swipes.\n- Is not restrictive during roleplays.",
- "qwen/qwen-2-7b-instruct:free": "Qwen2 7B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).\n\nNote: this is a free, rate-limited version of [Qwen 2 7B Instruct](/models/qwen/qwen-2-7b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "microsoft/phi-3-medium-4k-instruct": "Phi-3 4K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 128k context length, try [Phi-3 Medium 128K](/models/microsoft/phi-3-medium-128k-instruct).",
- "cognitivecomputations/dolphin-mixtral-8x22b": "Dolphin 2.9 is designed for instruction following, conversational, and coding. This model is a finetune of [Mixtral 8x22B Instruct](/models/mistralai/mixtral-8x22b-instruct). It features a 64k context length and was fine-tuned with a 16k sequence length using ChatML templates.\n\nThis model is a successor to [Dolphin Mixtral 8x7B](/models/cognitivecomputations/dolphin-mixtral-8x7b).\n\nThe model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored",
+ "cognitivecomputations/dolphin-mixtral-8x22b": "Dolphin 2.9 is designed for instruction following, conversational, and coding. This model is a finetune of [Mixtral 8x22B Instruct](/mistralai/mixtral-8x22b-instruct). It features a 64k context length and was fine-tuned with a 16k sequence length using ChatML templates.\n\nThis model is a successor to [Dolphin Mixtral 8x7B](/cognitivecomputations/dolphin-mixtral-8x7b).\n\nThe model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored",
"qwen/qwen-2-72b-instruct": "Qwen2 72B is a transformer-based model that excels in language understanding, multilingual capabilities, coding, mathematics, and reasoning.\n\nIt features SwiGLU activation, attention QKV bias, and group query attention. It is pretrained on extensive data with supervised finetuning and direct preference optimization.\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen2/) and [GitHub repo](https://github.com/QwenLM/Qwen2).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
- "openchat/openchat-8b": "OpenChat 8B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\nIt outperforms many similarly sized models including [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct) and various fine-tuned models. It excels in general conversation, coding assistance, and mathematical reasoning.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/models/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/models/openchat/openchat-8b).\n\n#open-source",
"nousresearch/hermes-2-pro-llama-3-8b": "Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.",
- "mistralai/mistral-7b-instruct-v0.3": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\nAn improved version of [Mistral 7B Instruct v0.2](/models/mistralai/mistral-7b-instruct-v0.2), with the following changes:\n\n- Extended vocabulary to 32768\n- Supports v3 Tokenizer\n- Supports function calling\n\nNOTE: Support for function calling depends on the provider.",
+ "mistralai/mistral-7b-instruct-v0.3": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\nAn improved version of [Mistral 7B Instruct v0.2](/mistralai/mistral-7b-instruct-v0.2), with the following changes:\n\n- Extended vocabulary to 32768\n- Supports v3 Tokenizer\n- Supports function calling\n\nNOTE: Support for function calling depends on the provider.",
+ "mistralai/mistral-7b-instruct:free": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*\n\n_These are free, rate-limited endpoints for [Mistral 7B Instruct](/mistralai/mistral-7b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"mistralai/mistral-7b-instruct": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*",
- "microsoft/phi-3-mini-128k-instruct:free": "Phi-3 Mini is a powerful 3.8B parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. This model is static, trained on an offline dataset with an October 2023 cutoff date.\n\nNote: this is a free, rate-limited version of [Phi-3 Mini 128K Instruct](/models/microsoft/phi-3-mini-128k-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits).",
+ "mistralai/mistral-7b-instruct:nitro": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*\n\n_These are higher-throughput endpoints for [Mistral 7B Instruct](/mistralai/mistral-7b-instruct). They may have higher prices._",
+ "microsoft/phi-3-mini-128k-instruct:free": "Phi-3 Mini is a powerful 3.8B parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. This model is static, trained on an offline dataset with an October 2023 cutoff date.\n\n_These are free, rate-limited endpoints for [Phi-3 Mini 128K Instruct](/microsoft/phi-3-mini-128k-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"microsoft/phi-3-mini-128k-instruct": "Phi-3 Mini is a powerful 3.8B parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. This model is static, trained on an offline dataset with an October 2023 cutoff date.",
- "microsoft/phi-3-medium-128k-instruct:free": "Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 4k context length, try [Phi-3 Medium 4K](/models/microsoft/phi-3-medium-4k-instruct).\n\nNote: this is a free, rate-limited version of [Phi-3 Medium 128K Instruct](/models/microsoft/phi-3-medium-128k-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "microsoft/phi-3-medium-128k-instruct": "Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 4k context length, try [Phi-3 Medium 4K](/models/microsoft/phi-3-medium-4k-instruct).",
+ "microsoft/phi-3-medium-128k-instruct:free": "Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 4k context length, try [Phi-3 Medium 4K](/microsoft/phi-3-medium-4k-instruct).\n\n_These are free, rate-limited endpoints for [Phi-3 Medium 128K Instruct](/microsoft/phi-3-medium-128k-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "microsoft/phi-3-medium-128k-instruct": "Phi-3 128K Medium is a powerful 14-billion parameter model designed for advanced language understanding, reasoning, and instruction following. Optimized through supervised fine-tuning and preference adjustments, it excels in tasks involving common sense, mathematics, logical reasoning, and code processing.\n\nAt time of release, Phi-3 Medium demonstrated state-of-the-art performance among lightweight models. In the MMLU-Pro eval, the model even comes close to a Llama3 70B level of performance.\n\nFor 4k context length, try [Phi-3 Medium 4K](/microsoft/phi-3-medium-4k-instruct).",
"neversleep/llama-3-lumimaid-70b": "The NeverSleep team is back, with a Llama 3 70B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.\n\nTo enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "deepseek/deepseek-coder": "DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model. It is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens.\n\nThe original V1 model was trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. It was pre-trained on project-level code corpus by employing a extra fill-in-the-blank task.",
- "deepseek/deepseek-chat": "DeepSeek-V2 Chat is a conversational finetune of DeepSeek-V2, a Mixture-of-Experts (MoE) language model. It comprises 236B total parameters, of which 21B are activated for each token.\n\nCompared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.\n\nDeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluations.",
- "perplexity/llama-3-sonar-large-32k-online": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is the online version of the [offline chat model](/models/perplexity/llama-3-sonar-large-32k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online",
- "perplexity/llama-3-sonar-large-32k-chat": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/models/perplexity/llama-3-sonar-large-32k-online) of this model has Internet access.",
- "perplexity/llama-3-sonar-small-32k-online": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is the online version of the [offline chat model](/models/perplexity/llama-3-sonar-small-32k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online",
- "perplexity/llama-3-sonar-small-32k-chat": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/models/perplexity/llama-3-sonar-small-32k-online) of this model has Internet access.",
"google/gemini-flash-1.5": "Gemini 1.5 Flash is a foundation model that performs well at a variety of multimodal tasks such as visual understanding, classification, summarization, and creating content from image, audio and video. It's adept at processing visual and text inputs such as photographs, documents, infographics, and screenshots.\n\nGemini 1.5 Flash is designed for high-volume, high-frequency tasks where cost and latency matter. On most common tasks, Flash achieves comparable quality to other Gemini Pro models at a significantly reduced cost. Flash is well-suited for applications like chat assistants and on-demand content generation where speed and scale matter.\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal",
- "meta-llama/llama-guard-2-8b": "This safeguard model has 8B parameters and is based on the Llama 3 family. Just like is predecessor, [LlamaGuard 1](https://huggingface.co/meta-llama/LlamaGuard-7b), it can do both prompt and response classification.\n\nLlamaGuard 2 acts as a normal LLM would, generating text that indicates whether the given input/output is safe/unsafe. If deemed unsafe, it will also share the content categories violated.\n\nFor best results, please use raw prompt input or the `/completions` endpoint, instead of the chat API.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3-70b": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This is the base 70B pre-trained version.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3-8b": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This is the base 8B pre-trained version.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "openai/gpt-4o-2024-05-13": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal",
- "openai/gpt-4o": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal",
- "allenai/olmo-7b-instruct": "OLMo 7B Instruct by the Allen Institute for AI is a model finetuned for question answering. It demonstrates **notable performance** across multiple benchmarks including TruthfulQA and ToxiGen.\n\n**Open Source**: The model, its code, checkpoints, logs are released under the [Apache 2.0 license](https://choosealicense.com/licenses/apache-2.0).\n\n- [Core repo (training, inference, fine-tuning etc.)](https://github.com/allenai/OLMo)\n- [Evaluation code](https://github.com/allenai/OLMo-Eval)\n- [Further fine-tuning code](https://github.com/allenai/open-instruct)\n- [Paper](https://arxiv.org/abs/2402.00838)\n- [Technical blog post](https://blog.allenai.org/olmo-open-language-model-87ccfc95f580)\n- [W&B Logs](https://wandb.ai/ai2-llm/OLMo-7B/reports/OLMo-7B--Vmlldzo2NzQyMzk5)",
- "qwen/qwen-4b-chat": "Qwen1.5 4B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
- "qwen/qwen-7b-chat": "Qwen1.5 7B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
- "qwen/qwen-14b-chat": "Qwen1.5 14B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
- "qwen/qwen-32b-chat": "Qwen1.5 32B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
+ "deepseek/deepseek-chat": "DeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions.\n\nDeepSeek-V2 Chat is a conversational finetune of DeepSeek-V2, a Mixture-of-Experts (MoE) language model. It comprises 236B total parameters, of which 21B are activated for each token.\n\nCompared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.\n\nDeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluations.",
+ "perplexity/llama-3-sonar-large-32k-online": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is the online version of the [offline chat model](/perplexity/llama-3-sonar-large-32k-chat). It is focused on delivering helpful, up-to-date, and factual responses. #online",
+ "perplexity/llama-3-sonar-large-32k-chat": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/perplexity/llama-3-sonar-large-32k-online) of this model has Internet access.",
+ "perplexity/llama-3-sonar-small-32k-chat": "Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.\n\nThis is a normal offline LLM, but the [online version](/perplexity/llama-3-sonar-small-32k-online) of this model has Internet access.",
+ "meta-llama/llama-guard-2-8b": "This safeguard model has 8B parameters and is based on the Llama 3 family. Just like is predecessor, [LlamaGuard 1](https://huggingface.co/meta-llama/LlamaGuard-7b), it can do both prompt and response classification.\n\nLlamaGuard 2 acts as a normal LLM would, generating text that indicates whether the given input/output is safe/unsafe. If deemed unsafe, it will also share the content categories violated.\n\nFor best results, please use raw prompt input or the `/completions` endpoint, instead of the chat API.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "openai/gpt-4o-2024-05-13": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)",
+ "openai/gpt-4o": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)",
+ "openai/gpt-4o:extended": "GPT-4o Extended is an experimental variant of GPT-4o with an extended max output tokens. This model supports only text input to text output.\n\n_These are extended-context endpoints for [GPT-4o](/openai/gpt-4o). They may have higher prices._",
"qwen/qwen-72b-chat": "Qwen1.5 72B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
"qwen/qwen-110b-chat": "Qwen1.5 110B is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:\n\n- Significant performance improvement in human preference for chat models\n- Multilingual support of both base and chat models\n- Stable support of 32K context length for models of all sizes\n\nFor more details, see this [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
- "meta-llama/llama-3-8b-instruct:free": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\nNote: this is a free, rate-limited version of [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "neversleep/llama-3-lumimaid-8b:extended": "The NeverSleep team is back, with a Llama 3 8B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.\n\nTo enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\nNote: this is an extended-context version of [Llama 3 Lumimaid 8B](/models/neversleep/llama-3-lumimaid-8b). It may have higher prices and different outputs.",
"neversleep/llama-3-lumimaid-8b": "The NeverSleep team is back, with a Llama 3 8B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.\n\nTo enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "snowflake/snowflake-arctic-instruct": "Arctic is a dense-MoE Hybrid transformer architecture pre-trained from scratch by the Snowflake AI Research Team. Arctic combines a 10B dense transformer model with a residual 128x3.66B MoE MLP resulting in 480B total and 17B active parameters chosen using a top-2 gating.\n\nTo read more about this model's release, [click here](https://www.snowflake.com/blog/arctic-open-efficient-foundation-language-models-snowflake/).",
- "fireworks/firellava-13b": "A blazing fast vision-language model, FireLLaVA quickly understands both text and images. It achieves impressive chat skills in tests, and was designed to mimic multimodal GPT-4.\n\nThe first commercially permissive open source LLaVA model, trained entirely on open source LLM generated instruction following data.",
- "lynn/soliloquy-l3": "Soliloquy-L3 v2 is a fast, highly capable roleplaying model designed for immersive, dynamic experiences. Trained on over 250 million tokens of roleplaying data, Soliloquy-L3 has a vast knowledge base, rich literary expression, and support for up to 24k context length. It outperforms existing ~13B models, delivering enhanced roleplaying capabilities.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3-8b-instruct:extended": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\nNote: this is an extended-context version of [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct). It may have higher prices and different outputs.",
+ "neversleep/llama-3-lumimaid-8b:extended": "The NeverSleep team is back, with a Llama 3 8B finetune trained on their curated roleplay data. Striking a balance between eRP and RP, Lumimaid was designed to be serious, yet uncensored when necessary.\n\nTo enhance it's overall intelligence and chat capability, roughly 40% of the training data was not roleplay. This provides a breadth of knowledge to access, while still keeping roleplay as the primary strength.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\n_These are extended-context endpoints for [Llama 3 Lumimaid v0.1 8B](/neversleep/llama-3-lumimaid-8b). They may have higher prices._",
"sao10k/fimbulvetr-11b-v2": "Creative writing model, routed with permission. It's fast, it keeps the conversation going, and it stays in character.\n\nIf you submit a raw prompt, you can use Alpaca or Vicuna formats.",
- "meta-llama/llama-3-70b-instruct:nitro": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\nNote: this is a higher-throughput version of [Llama 3 70B Instruct](/models/meta-llama/llama-3-70b-instruct). It may have higher prices and slightly different outputs.",
- "meta-llama/llama-3-8b-instruct:nitro": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).\n\nNote: this is a higher-throughput version of [Llama 3 8B Instruct](/models/meta-llama/llama-3-8b-instruct). It may have higher prices and slightly different outputs.",
- "meta-llama/llama-3-70b-instruct": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "meta-llama/llama-3-8b-instruct": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nTo read more about the model release, [click here](https://ai.meta.com/blog/meta-llama-3/). Usage of this model is subject to [Meta's Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).",
- "mistralai/mixtral-8x22b-instruct": "Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include:\n- strong math, coding, and reasoning\n- large context length (64k)\n- fluency in English, French, Italian, German, and Spanish\n\nSee benchmarks on the launch announcement [here](https://mistral.ai/news/mixtral-8x22b/).\n#moe",
- "microsoft/wizardlm-2-7b": "WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger opensource leading models\n\nIt is a finetune of [Mistral 7B Instruct](/models/mistralai/mistral-7b-instruct), using the same technique as [WizardLM-2 8x22B](/models/microsoft/wizardlm-2-8x22b).\n\nTo read more about the model release, [click here](https://wizardlm.github.io/WizardLM2/).\n\n#moe",
- "microsoft/wizardlm-2-8x22b": "WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.\n\nIt is an instruct finetune of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b).\n\nTo read more about the model release, [click here](https://wizardlm.github.io/WizardLM2/).\n\n#moe",
- "undi95/toppy-m-7b:nitro": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/models/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored\n\nNote: this is a higher-throughput version of [Toppy M 7B](/models/undi95/toppy-m-7b). It may have higher prices and slightly different outputs.",
- "mistralai/mixtral-8x22b": "Mixtral 8x22B is a large-scale language model from Mistral AI. It consists of 8 experts, each 22 billion parameters, with each token using 2 experts at a time.\n\nIt was released via [X](https://twitter.com/MistralAI/status/1777869263778291896).\n\n#moe",
+ "meta-llama/llama-3-70b-instruct": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3-70b-instruct:nitro": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are higher-throughput endpoints for [Llama 3 70B Instruct](/meta-llama/llama-3-70b-instruct). They may have higher prices._",
+ "meta-llama/llama-3-8b-instruct:free": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are free, rate-limited endpoints for [Llama 3 8B Instruct](/meta-llama/llama-3-8b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "meta-llama/llama-3-8b-instruct": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).",
+ "meta-llama/llama-3-8b-instruct:nitro": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are higher-throughput endpoints for [Llama 3 8B Instruct](/meta-llama/llama-3-8b-instruct). They may have higher prices._",
+ "meta-llama/llama-3-8b-instruct:extended": "Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.\n\nIt has demonstrated strong performance compared to leading closed-source models in human evaluations.\n\nUsage of this model is subject to [Meta's Acceptable Use Policy](https://www.llama.com/llama3/use-policy/).\n\n_These are extended-context endpoints for [Llama 3 8B Instruct](/meta-llama/llama-3-8b-instruct). They may have higher prices._",
+ "mistralai/mixtral-8x22b-instruct": "Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include:\n- strong math, coding, and reasoning\n- large context length (64k)\n- fluency in English, French, Italian, German, and Spanish\n\nSee benchmarks on the launch announcement [here](https://mistral.ai/news/mixtral-8x22b/).\n#moe",
+ "microsoft/wizardlm-2-7b": "WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger opensource leading models\n\nIt is a finetune of [Mistral 7B Instruct](/mistralai/mistral-7b-instruct), using the same technique as [WizardLM-2 8x22B](/microsoft/wizardlm-2-8x22b).\n\nTo read more about the model release, [click here](https://wizardlm.github.io/WizardLM2/).\n\n#moe",
+ "microsoft/wizardlm-2-8x22b": "WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models.\n\nIt is an instruct finetune of [Mixtral 8x22B](/mistralai/mixtral-8x22b).\n\nTo read more about the model release, [click here](https://wizardlm.github.io/WizardLM2/).\n\n#moe",
"google/gemini-pro-1.5": "Google's latest multimodal model, supporting image and video in text or chat prompts.\n\nOptimized for language tasks including:\n\n- Code generation\n- Text generation\n- Text editing\n- Problem solving\n- Recommendations\n- Information extraction\n- Data extraction or generation\n- AI agents\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal",
"openai/gpt-4-turbo": "The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.\n\nTraining data: up to December 2023.",
"cohere/command-r-plus": "Command R+ is a new, 104B-parameter LLM from Cohere. It's useful for roleplay, general consumer usecases, and Retrieval Augmented Generation (RAG).\n\nIt offers multilingual support for ten key languages to facilitate global business operations. See benchmarks and the launch post [here](https://txt.cohere.com/command-r-plus-microsoft-azure/).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
- "databricks/dbrx-instruct": "DBRX is a new open source large language model developed by Databricks. At 132B, it outperforms existing open source LLMs like Llama 2 70B and [Mixtral-8x7b](/models/mistralai/mixtral-8x7b) on standard industry benchmarks for language understanding, programming, math, and logic.\n\nIt uses a fine-grained mixture-of-experts (MoE) architecture. 36B parameters are active on any input. It was pre-trained on 12T tokens of text and code data. Compared to other open MoE models like Mixtral-8x7B and Grok-1, DBRX is fine-grained, meaning it uses a larger number of smaller experts.\n\nSee the launch announcement and benchmark results [here](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm).\n\n#moe",
+ "cohere/command-r-plus-04-2024": "Command R+ is a new, 104B-parameter LLM from Cohere. It's useful for roleplay, general consumer usecases, and Retrieval Augmented Generation (RAG).\n\nIt offers multilingual support for ten key languages to facilitate global business operations. See benchmarks and the launch post [here](https://txt.cohere.com/command-r-plus-microsoft-azure/).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
+ "databricks/dbrx-instruct": "DBRX is a new open source large language model developed by Databricks. At 132B, it outperforms existing open source LLMs like Llama 2 70B and [Mixtral-8x7b](/mistralai/mixtral-8x7b) on standard industry benchmarks for language understanding, programming, math, and logic.\n\nIt uses a fine-grained mixture-of-experts (MoE) architecture. 36B parameters are active on any input. It was pre-trained on 12T tokens of text and code data. Compared to other open MoE models like Mixtral-8x7B and Grok-1, DBRX is fine-grained, meaning it uses a larger number of smaller experts.\n\nSee the launch announcement and benchmark results [here](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm).\n\n#moe",
"sophosympatheia/midnight-rose-70b": "A merge with a complex family tree, this model was crafted for roleplaying and storytelling. Midnight Rose is a successor to Rogue Rose and Aurora Nights and improves upon them both. It wants to produce lengthy output by default and is the best creative writing merge produced so far by sophosympatheia.\n\nDescending from earlier versions of Midnight Rose and [Wizard Tulu Dolphin 70B](https://huggingface.co/sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0), it inherits the best qualities of each.",
"cohere/command-r": "Command-R is a 35B parameter model that performs conversational language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents.\n\nRead the launch post [here](https://txt.cohere.com/command-r/).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
"cohere/command": "Command is an instruction-following conversational model that performs language tasks with high quality, more reliably and with a longer context than our base generative models.\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
- "anthropic/claude-3-haiku:beta": "This is a lower-latency version of [Claude 3 Haiku](/models/anthropic/claude-3-haiku), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 3 Haiku is Anthropic's fastest and most compact model for\nnear-instant responsiveness. Quick and accurate targeted performance.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku)\n\n#multimodal",
"anthropic/claude-3-haiku": "Claude 3 Haiku is Anthropic's fastest and most compact model for\nnear-instant responsiveness. Quick and accurate targeted performance.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku)\n\n#multimodal",
- "google/gemma-7b-it:nitro": "Gemma by Google is an advanced, open-source language model family, leveraging the latest in decoder-only, text-to-text technology. It offers English language capabilities across text generation tasks like question answering, summarization, and reasoning. The Gemma 7B variant is comparable in performance to leading open source models.\n\nUsage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).\n\nNote: this is a higher-throughput version of [Gemma 7B](/models/google/gemma-7b-it). It may have higher prices and slightly different outputs.",
- "mistralai/mistral-7b-instruct:nitro": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*\n\nNote: this is a higher-throughput version of [Mistral 7B Instruct](/models/mistralai/mistral-7b-instruct). It may have higher prices and slightly different outputs.",
- "mistralai/mixtral-8x7b-instruct:nitro": "A pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.\n\nInstruct model fine-tuned by Mistral. #moe\n\nNote: this is a higher-throughput version of [Mixtral 8x7B Instruct](/models/mistralai/mixtral-8x7b-instruct). It may have higher prices and slightly different outputs.",
- "gryphe/mythomax-l2-13b:nitro": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge\n\nNote: this is a higher-throughput version of [MythoMax 13B](/models/gryphe/mythomax-l2-13b). It may have higher prices and slightly different outputs.",
- "anthropic/claude-3-sonnet:beta": "This is a lower-latency version of [Claude 3 Sonnet](/models/anthropic/claude-3-sonnet), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal",
- "anthropic/claude-3-opus:beta": "This is a lower-latency version of [Claude 3 Opus](/models/anthropic/claude-3-opus), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 3 Opus is Anthropic's most powerful model for highly complex tasks. It boasts top-level performance, intelligence, fluency, and understanding.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal",
+ "anthropic/claude-3-haiku:beta": "Claude 3 Haiku is Anthropic's fastest and most compact model for\nnear-instant responsiveness. Quick and accurate targeted performance.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku)\n\n#multimodal\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-3-haiku) variant._",
"anthropic/claude-3-sonnet": "Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal",
+ "anthropic/claude-3-sonnet:beta": "Claude 3 Sonnet is an ideal balance of intelligence and speed for enterprise workloads. Maximum utility at a lower price, dependable, balanced for scaled deployments.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-3-sonnet) variant._",
"anthropic/claude-3-opus": "Claude 3 Opus is Anthropic's most powerful model for highly complex tasks. It boasts top-level performance, intelligence, fluency, and understanding.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal",
- "mistralai/mistral-large": "This is Mistral AI's closed-source, flagship model. It's powered by a closed-source prototype and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large/).\n\nIt is fluent in English, French, Spanish, German, and Italian, with high grammatical accuracy, and its 32K tokens context window allows precise information recall from large documents.",
- "google/gemma-7b-it:free": "Gemma by Google is an advanced, open-source language model family, leveraging the latest in decoder-only, text-to-text technology. It offers English language capabilities across text generation tasks like question answering, summarization, and reasoning. The Gemma 7B variant is comparable in performance to leading open source models.\n\nUsage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).\n\nNote: this is a free, rate-limited version of [Gemma 7B](/models/google/gemma-7b-it). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "google/gemma-7b-it": "Gemma by Google is an advanced, open-source language model family, leveraging the latest in decoder-only, text-to-text technology. It offers English language capabilities across text generation tasks like question answering, summarization, and reasoning. The Gemma 7B variant is comparable in performance to leading open source models.\n\nUsage of Gemma is subject to Google's [Gemma Terms of Use](https://ai.google.dev/gemma/terms).",
- "nousresearch/nous-hermes-2-mistral-7b-dpo": "This is the flagship 7B Hermes model, a Direct Preference Optimization (DPO) of [Teknium/OpenHermes-2.5-Mistral-7B](/models/teknium/openhermes-2.5-mistral-7b). It shows improvement across the board on all benchmarks tested - AGIEval, BigBench Reasoning, GPT4All, and TruthfulQA.\n\nThe model prior to DPO was trained on 1,000,000 instructions/chats of GPT-4 quality or better, primarily synthetic data as well as other high quality datasets.",
- "meta-llama/codellama-70b-instruct": "Code Llama is a family of large language models for code. This one is based on [Llama 2 70B](/models/meta-llama/llama-2-70b-chat) and provides zero-shot instruction-following ability for programming tasks.",
- "recursal/eagle-7b": "Eagle 7B is trained on 1.1 Trillion Tokens across 100+ world languages (70% English, 15% multilang, 15% code).\n\n- Built on the [RWKV-v5](/models?q=rwkv) architecture (a linear transformer with 10-100x+ lower inference cost)\n- Ranks as the world's greenest 7B model (per token)\n- Outperforms all 7B class models in multi-lingual benchmarks\n- Approaches Falcon (1.5T), LLaMA2 (2T), Mistral (>2T?) level of performance in English evals\n- Trade blows with MPT-7B (1T) in English evals\n- All while being an [\"Attention-Free Transformer\"](https://www.isattentionallyouneed.com/)\n\nEagle 7B models are provided for free, by [Recursal.AI](https://recursal.ai), for the beta period till end of March 2024\n\nFind out more [here](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)\n\n[rnn](/models?q=rwkv)",
+ "anthropic/claude-3-opus:beta": "Claude 3 Opus is Anthropic's most powerful model for highly complex tasks. It boasts top-level performance, intelligence, fluency, and understanding.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-family)\n\n#multimodal\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-3-opus) variant._",
+ "cohere/command-r-03-2024": "Command-R is a 35B parameter model that performs conversational language tasks at a higher quality, more reliably, and with a longer context than previous models. It can be used for complex workflows like code generation, retrieval augmented generation (RAG), tool use, and agents.\n\nRead the launch post [here](https://txt.cohere.com/command-r/).\n\nUse of this model is subject to Cohere's [Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).",
+ "mistralai/mistral-large": "This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/).\n\nIt is fluent in English, French, Spanish, German, and Italian, with high grammatical accuracy, and its long context window allows precise information recall from large documents.",
"openai/gpt-4-turbo-preview": "The preview GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Dec 2023.\n\n**Note:** heavily rate limited by OpenAI while in preview.",
"openai/gpt-3.5-turbo-0613": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.",
- "undi95/remm-slerp-l2-13b:extended": "A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge\n\nNote: this is an extended-context version of [ReMM SLERP 13B](/models/undi95/remm-slerp-l2-13b). It may have higher prices and different outputs.",
- "nousresearch/nous-hermes-2-mixtral-8x7b-sft": "Nous Hermes 2 Mixtral 8x7B SFT is the supervised finetune only version of [the Nous Research model](/models/nousresearch/nous-hermes-2-mixtral-8x7b-dpo) trained over the [Mixtral 8x7B MoE LLM](/models/mistralai/mixtral-8x7b).\n\nThe model was trained on over 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.\n\n#moe",
- "nousresearch/nous-hermes-2-mixtral-8x7b-dpo": "Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](/models/mistralai/mixtral-8x7b).\n\nThe model was trained on over 1,000,000 entries of primarily [GPT-4](/models/openai/gpt-4) generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.\n\n#moe",
+ "nousresearch/nous-hermes-2-mixtral-8x7b-dpo": "Nous Hermes 2 Mixtral 8x7B DPO is the new flagship Nous Research model trained over the [Mixtral 8x7B MoE LLM](/mistralai/mixtral-8x7b).\n\nThe model was trained on over 1,000,000 entries of primarily [GPT-4](/openai/gpt-4) generated data, as well as other high quality data from open datasets across the AI landscape, achieving state of the art performance on a variety of tasks.\n\n#moe",
"mistralai/mistral-medium": "This is Mistral AI's closed-source, medium-sided model. It's powered by a closed-source prototype and excels at reasoning, code, JSON, chat, and more. In benchmarks, it compares with many of the flagship models of other companies.",
- "mistralai/mistral-small": "This model is currently powered by Mixtral-8X7B-v0.1, a sparse mixture of experts model with 12B active parameters. It has better reasoning, exhibits more capabilities, can produce and reason about code, and is multiligual, supporting English, French, German, Italian, and Spanish.\n#moe",
- "mistralai/mistral-tiny": "This model is currently powered by Mistral-7B-v0.2, and incorporates a \"better\" fine-tuning than [Mistral 7B](/models/mistralai/mistral-7b-instruct-v0.1), inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.",
- "austism/chronos-hermes-13b": "A 75/25 merge of [Chronos 13b v2](https://huggingface.co/elinas/chronos-13b-v2) and [Nous Hermes Llama2 13b](/models/nousresearch/nous-hermes-llama2-13b). This offers the imaginative writing style of Chronos while retaining coherency. Outputs are long and use exceptional prose. #merge",
- "nousresearch/nous-hermes-yi-34b": "Nous Hermes 2 Yi 34B was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape.\n\nNous-Hermes 2 on Yi 34B outperforms all Nous-Hermes & Open-Hermes models of the past, achieving new heights in all benchmarks for a Nous Research LLM as well as surpassing many popular finetunes.",
+ "mistralai/mistral-small": "Cost-efficient, fast, and reliable option for use cases such as translation, summarization, and sentiment analysis.",
+ "mistralai/mistral-tiny": "This model is currently powered by Mistral-7B-v0.2, and incorporates a \"better\" fine-tuning than [Mistral 7B](/mistralai/mistral-7b-instruct-v0.1), inspired by community work. It's best used for large batch processing tasks where cost is a significant factor but reasoning capabilities are not crucial.",
"mistralai/mistral-7b-instruct-v0.2": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\nAn improved version of [Mistral 7B Instruct](/modelsmistralai/mistral-7b-instruct-v0.1), with the following changes:\n\n- 32k context window (vs 8k context in v0.1)\n- Rope-theta = 1e6\n- No Sliding-Window Attention",
- "cognitivecomputations/dolphin-mixtral-8x7b": "This is a 16k context fine-tune of [Mixtral-8x7b](/models/mistralai/mixtral-8x7b). It excels in coding tasks due to extensive training with coding data and is known for its obedience, although it lacks DPO tuning.\n\nThe model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored",
- "google/gemini-pro-vision": "Google's flagship multimodal model, supporting image and video in text or chat prompts for a text or code response.\n\nSee the benchmarks and prompting guidelines from [Deepmind](https://deepmind.google/technologies/gemini/).\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal",
+ "cognitivecomputations/dolphin-mixtral-8x7b": "This is a 16k context fine-tune of [Mixtral-8x7b](/mistralai/mixtral-8x7b). It excels in coding tasks due to extensive training with coding data and is known for its obedience, although it lacks DPO tuning.\n\nThe model is uncensored and is stripped of alignment and bias. It requires an external alignment layer for ethical use. Users are cautioned to use this highly compliant model responsibly, as detailed in a blog post about uncensored models at [erichartford.com/uncensored-models](https://erichartford.com/uncensored-models).\n\n#moe #uncensored",
"google/gemini-pro": "Google's flagship text generation model. Designed to handle natural language tasks, multiturn text and code chat, and code generation.\n\nSee the benchmarks and prompting guidelines from [Deepmind](https://deepmind.google/technologies/gemini/).\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).",
- "recursal/rwkv-5-3b-ai-town": "This is an [RWKV 3B model](/models/rwkv/rwkv-5-world-3b) finetuned specifically for the [AI Town](https://github.com/a16z-infra/ai-town) project.\n\n[RWKV](https://wiki.rwkv.com) is an RNN (recurrent neural network) with transformer-level performance. It aims to combine the best of RNNs and transformers - great performance, fast inference, low VRAM, fast training, \"infinite\" context length, and free sentence embedding.\n\nRWKV 3B models are provided for free, by Recursal.AI, for the beta period. More details [here](https://substack.recursal.ai/p/public-rwkv-3b-model-via-openrouter).\n\n#rnn",
- "rwkv/rwkv-5-world-3b": "[RWKV](https://wiki.rwkv.com) is an RNN (recurrent neural network) with transformer-level performance. It aims to combine the best of RNNs and transformers - great performance, fast inference, low VRAM, fast training, \"infinite\" context length, and free sentence embedding.\n\nRWKV-5 is trained on 100+ world languages (70% English, 15% multilang, 15% code).\n\nRWKV 3B models are provided for free, by Recursal.AI, for the beta period. More details [here](https://substack.recursal.ai/p/public-rwkv-3b-model-via-openrouter).\n\n#rnn",
+ "google/gemini-pro-vision": "Google's flagship multimodal model, supporting image and video in text or chat prompts for a text or code response.\n\nSee the benchmarks and prompting guidelines from [Deepmind](https://deepmind.google/technologies/gemini/).\n\nUsage of Gemini is subject to Google's [Gemini Terms of Use](https://ai.google.dev/terms).\n\n#multimodal",
"mistralai/mixtral-8x7b-instruct": "A pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.\n\nInstruct model fine-tuned by Mistral. #moe",
- "mistralai/mixtral-8x7b": "A pretrained generative Sparse Mixture of Experts, by Mistral AI. Incorporates 8 experts (feed-forward networks) for a total of 47B parameters. Base model (not fine-tuned for instructions) - see [Mixtral 8x7B Instruct](/models/mistralai/mixtral-8x7b-instruct) for an instruct-tuned model.\n\n#moe",
- "togethercomputer/stripedhyena-hessian-7b": "This is the base model variant of the [StripedHyena series](/models?q=stripedhyena), developed by Together.\n\nStripedHyena uses a new architecture that competes with traditional Transformers, particularly in long-context data processing. It combines attention mechanisms with gated convolutions for improved speed, efficiency, and scaling. This model marks an advancement in AI architecture for sequence modeling tasks.",
- "togethercomputer/stripedhyena-nous-7b": "This is the chat model variant of the [StripedHyena series](/models?q=stripedhyena) developed by Together in collaboration with Nous Research.\n\nStripedHyena uses a new architecture that competes with traditional Transformers, particularly in long-context data processing. It combines attention mechanisms with gated convolutions for improved speed, efficiency, and scaling. This model marks a significant advancement in AI architecture for sequence modeling tasks.",
- "koboldai/psyfighter-13b-2": "The v2 of [Psyfighter](/models/jebcarter/psyfighter-13b) - a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows, made possible thanks to the KoboldAI merge request service.\n\nThe intent was to add medical data to supplement the model's fictional ability with more details on anatomy and mental states. This model should not be used for medical advice or therapy because of its high likelihood of pulling in fictional data.\n\nIt's a merge between:\n\n- [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter)\n- [Doctor-Shotgun/cat-v1.0-13b](https://huggingface.co/Doctor-Shotgun/cat-v1.0-13b)\n- [Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged](https://huggingface.co/Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged).\n\n#merge",
- "gryphe/mythomist-7b": "From the creator of [MythoMax](/models/gryphe/mythomax-l2-13b), merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.\n\nIt combines [Neural Chat 7B](/models/intel/neural-chat-7b), Airoboros 7b, [Toppy M 7B](/models/undi95/toppy-m-7b), [Zepher 7b beta](/models/huggingfaceh4/zephyr-7b-beta), [Nous Capybara 34B](/models/nousresearch/nous-capybara-34b), [OpenHeremes 2.5](/models/teknium/openhermes-2.5-mistral-7b), and many others.\n\n#merge",
- "01-ai/yi-6b": "The Yi series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). This is the base 6B parameter model.",
- "01-ai/yi-34b": "The Yi series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). This is the base 34B parameter model.",
- "01-ai/yi-34b-chat": "The Yi series models are large language models trained from scratch by developers at [01.AI](https://01.ai/). This 34B parameter model has been instruct-tuned for chat.",
- "nousresearch/nous-capybara-7b:free": "The Capybara series is a collection of datasets and models made by fine-tuning on data created by Nous, mostly in-house.\n\nV1.9 uses unalignment techniques for more consistent and dynamic control. It also leverages a significantly better foundation model, [Mistral 7B](/models/mistralai/mistral-7b-instruct-v0.1).\n\nNote: this is a free, rate-limited version of [Capybara 7B](/models/nousresearch/nous-capybara-7b). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "nousresearch/nous-capybara-7b": "The Capybara series is a collection of datasets and models made by fine-tuning on data created by Nous, mostly in-house.\n\nV1.9 uses unalignment techniques for more consistent and dynamic control. It also leverages a significantly better foundation model, [Mistral 7B](/models/mistralai/mistral-7b-instruct-v0.1).",
- "openchat/openchat-7b:free": "OpenChat 7B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/models/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/models/openchat/openchat-8b).\n\n#open-source\n\nNote: this is a free, rate-limited version of [OpenChat 3.5 7B](/models/openchat/openchat-7b). Outputs may be cached. Read about rate limits [here](/docs/limits).",
- "openchat/openchat-7b": "OpenChat 7B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/models/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/models/openchat/openchat-8b).\n\n#open-source",
- "gryphe/mythomist-7b:free": "From the creator of [MythoMax](/models/gryphe/mythomax-l2-13b), merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.\n\nIt combines [Neural Chat 7B](/models/intel/neural-chat-7b), Airoboros 7b, [Toppy M 7B](/models/undi95/toppy-m-7b), [Zepher 7b beta](/models/huggingfaceh4/zephyr-7b-beta), [Nous Capybara 34B](/models/nousresearch/nous-capybara-34b), [OpenHeremes 2.5](/models/teknium/openhermes-2.5-mistral-7b), and many others.\n\n#merge\n\nNote: this is a free, rate-limited version of [MythoMist 7B](/models/gryphe/mythomist-7b). Outputs may be cached. Read about rate limits [here](/docs/limits).",
+ "mistralai/mixtral-8x7b-instruct:nitro": "A pretrained generative Sparse Mixture of Experts, by Mistral AI, for chat and instruction use. Incorporates 8 experts (feed-forward networks) for a total of 47 billion parameters.\n\nInstruct model fine-tuned by Mistral. #moe\n\n_These are higher-throughput endpoints for [Mixtral 8x7B Instruct](/mistralai/mixtral-8x7b-instruct). They may have higher prices._",
+ "mistralai/mixtral-8x7b": "A pretrained generative Sparse Mixture of Experts, by Mistral AI. Incorporates 8 experts (feed-forward networks) for a total of 47B parameters. Base model (not fine-tuned for instructions) - see [Mixtral 8x7B Instruct](/mistralai/mixtral-8x7b-instruct) for an instruct-tuned model.\n\n#moe",
+ "gryphe/mythomist-7b:free": "From the creator of [MythoMax](/gryphe/mythomax-l2-13b), merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.\n\nIt combines [Neural Chat 7B](/intel/neural-chat-7b), Airoboros 7b, [Toppy M 7B](/undi95/toppy-m-7b), [Zepher 7b beta](/huggingfaceh4/zephyr-7b-beta), [Nous Capybara 34B](/nousresearch/nous-capybara-34b), [OpenHeremes 2.5](/teknium/openhermes-2.5-mistral-7b), and many others.\n\n#merge\n\n_These are free, rate-limited endpoints for [MythoMist 7B](/gryphe/mythomist-7b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "gryphe/mythomist-7b": "From the creator of [MythoMax](/gryphe/mythomax-l2-13b), merges a suite of models to reduce word anticipation, ministrations, and other undesirable words in ChatGPT roleplaying data.\n\nIt combines [Neural Chat 7B](/intel/neural-chat-7b), Airoboros 7b, [Toppy M 7B](/undi95/toppy-m-7b), [Zepher 7b beta](/huggingfaceh4/zephyr-7b-beta), [Nous Capybara 34B](/nousresearch/nous-capybara-34b), [OpenHeremes 2.5](/teknium/openhermes-2.5-mistral-7b), and many others.\n\n#merge",
+ "openchat/openchat-7b:free": "OpenChat 7B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/openchat/openchat-8b).\n\n#open-source\n\n_These are free, rate-limited endpoints for [OpenChat 3.5 7B](/openchat/openchat-7b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "openchat/openchat-7b": "OpenChat 7B is a library of open-source language models, fine-tuned with \"C-RLFT (Conditioned Reinforcement Learning Fine-Tuning)\" - a strategy inspired by offline reinforcement learning. It has been trained on mixed-quality data without preference labels.\n\n- For OpenChat fine-tuned on Mistral 7B, check out [OpenChat 7B](/openchat/openchat-7b).\n- For OpenChat fine-tuned on Llama 8B, check out [OpenChat 8B](/openchat/openchat-8b).\n\n#open-source",
"neversleep/noromaid-20b": "A collab between IkariDev and Undi. This merge is suitable for RP, ERP, and general knowledge.\n\n#merge #uncensored",
- "intel/neural-chat-7b": "A fine-tuned model based on [mistralai/Mistral-7B-v0.1](/models/mistralai/mistral-7b-instruct-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), aligned with DPO algorithm. For more details, refer to the blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).",
- "anthropic/claude-2.1:beta": "This is a lower-latency version of [Claude v2.1](/models/anthropic/claude-2.1), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.",
- "anthropic/claude-2:beta": "This is a lower-latency version of [Claude v2](/models/anthropic/claude-2), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nClaude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.",
"anthropic/claude-instant-1.1": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
"anthropic/claude-2.1": "Claude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.",
+ "anthropic/claude-2.1:beta": "Claude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-2.1) variant._",
"anthropic/claude-2": "Claude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.",
- "teknium/openhermes-2.5-mistral-7b": "A continuation of [OpenHermes 2 model](/models/teknium/openhermes-2-mistral-7b), trained on additional code datasets.\nPotentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.",
- "openai/gpt-4-vision-preview": "Ability to understand images, in addition to all other [GPT-4 Turbo capabilties](/models/openai/gpt-4-turbo). Training data: up to Apr 2023.\n\n**Note:** heavily rate limited by OpenAI while in preview.\n\n#multimodal",
+ "anthropic/claude-2:beta": "Claude 2 delivers advancements in key capabilities for enterprises\u2014including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and a new beta feature: tool use.\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-2) variant._",
+ "teknium/openhermes-2.5-mistral-7b": "A continuation of [OpenHermes 2 model](/teknium/openhermes-2-mistral-7b), trained on additional code datasets.\nPotentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.",
+ "openai/gpt-4-vision-preview": "Ability to understand images, in addition to all other [GPT-4 Turbo capabilties](/openai/gpt-4-turbo). Training data: up to Apr 2023.\n\n**Note:** heavily rate limited by OpenAI while in preview.\n\n#multimodal",
"lizpreciatior/lzlv-70b-fp16-hf": "A Mythomax/MLewd_13B-style merge of selected 70B models.\nA multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.\n\n#merge #uncensored",
- "undi95/toppy-m-7b:free": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/models/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored\n\nNote: this is a free, rate-limited version of [Toppy M 7B](/models/undi95/toppy-m-7b). Outputs may be cached. Read about rate limits [here](/docs/limits).",
"alpindale/goliath-120b": "A large LLM created by combining two fine-tuned Llama 70B models into one 120B model. Combines Xwin and Euryale.\n\nCredits to\n- [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).\n- [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios.\n\n#merge",
- "undi95/toppy-m-7b": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/models/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored",
+ "undi95/toppy-m-7b:free": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored\n\n_These are free, rate-limited endpoints for [Toppy M 7B](/undi95/toppy-m-7b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
+ "undi95/toppy-m-7b": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored",
+ "undi95/toppy-m-7b:nitro": "A wild 7B parameter model that merges several models using the new task_arithmetic merge method from mergekit.\nList of merged models:\n- NousResearch/Nous-Capybara-7B-V1.9\n- [HuggingFaceH4/zephyr-7b-beta](/huggingfaceh4/zephyr-7b-beta)\n- lemonilia/AshhLimaRP-Mistral-7B\n- Vulkane/120-Days-of-Sodom-LoRA-Mistral-7b\n- Undi95/Mistral-pippa-sharegpt-7b-qlora\n\n#merge #uncensored\n\n_These are higher-throughput endpoints for [Toppy M 7B](/undi95/toppy-m-7b). They may have higher prices._",
"openrouter/auto": "Depending on their size, subject, and complexity, your prompts will be sent to [Llama 3 70B Instruct](/models/meta-llama/llama-3-70b-instruct), [Claude 3.5 Sonnet (self-moderated)](/models/anthropic/claude-3.5-sonnet:beta) or [GPT-4o](/models/openai/gpt-4o). To see which model was used, visit [Activity](/activity).\n\nA major redesign of this router is coming soon. Stay tuned on [Discord](https://discord.gg/fVyRaUDgxW) for updates.",
"openai/gpt-4-1106-preview": "The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.\n\nTraining data: up to April 2023.",
"openai/gpt-3.5-turbo-1106": "An older GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Sep 2021.",
- "huggingfaceh4/zephyr-7b-beta:free": "Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-\u03b2 is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](/models/mistralai/mistral-7b-instruct-v0.1) that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO).\n\nNote: this is a free, rate-limited version of [Zephyr 7B](/models/huggingfaceh4/zephyr-7b-beta). Outputs may be cached. Read about rate limits [here](/docs/limits).",
"google/palm-2-codechat-bison-32k": "PaLM 2 fine-tuned for chatbot conversations that help with code-related questions.",
"google/palm-2-chat-bison-32k": "PaLM 2 is a language model by Google with improved multilingual, reasoning and coding capabilities.",
- "teknium/openhermes-2-mistral-7b": "Trained on 900k instructions, surpasses all previous versions of Hermes 13B and below, and matches 70B on some benchmarks. Hermes 2 has strong multiturn chat skills and system prompt capabilities.",
- "open-orca/mistral-7b-openorca": "A fine-tune of Mistral using the OpenOrca dataset. First 7B model to beat all other models <30B.",
"jondurbin/airoboros-l2-70b": "A Llama 2 70B fine-tune using synthetic data (the Airoboros dataset).\n\nCurrently based on [jondurbin/airoboros-l2-70b](https://huggingface.co/jondurbin/airoboros-l2-70b-2.2.1), but might get updated in the future.",
- "gryphe/mythomax-l2-13b:extended": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge\n\nNote: this is an extended-context version of [MythoMax 13B](/models/gryphe/mythomax-l2-13b). It may have higher prices and different outputs.",
- "xwin-lm/xwin-lm-70b": "Xwin-LM aims to develop and open-source alignment tech for LLMs. Our first release, built-upon on the [Llama2](/models/${Model.Llama_2_13B_Chat}) base models, ranked TOP-1 on AlpacaEval. Notably, it's the first to surpass [GPT-4](/models/${Model.GPT_4}) on this benchmark. The project will be continuously updated.",
- "mistralai/mistral-7b-instruct:free": "A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.\n\n*Mistral 7B Instruct has multiple version variants, and this is intended to be the latest version.*\n\nNote: this is a free, rate-limited version of [Mistral 7B Instruct](/models/mistralai/mistral-7b-instruct). Outputs may be cached. Read about rate limits [here](/docs/limits).",
+ "xwin-lm/xwin-lm-70b": "Xwin-LM aims to develop and open-source alignment tech for LLMs. Our first release, built-upon on the [Llama2](/${Model.Llama_2_13B_Chat}) base models, ranked TOP-1 on AlpacaEval. Notably, it's the first to surpass [GPT-4](/${Model.GPT_4}) on this benchmark. The project will be continuously updated.",
"mistralai/mistral-7b-instruct-v0.1": "A 7.3B parameter model that outperforms Llama 2 13B on all benchmarks, with optimizations for speed and context length.",
"openai/gpt-3.5-turbo-instruct": "This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.",
"pygmalionai/mythalion-13b": "A blend of the new Pygmalion-13b and MythoMax. #merge",
@@ -826,25 +885,26 @@ const selectedModelDisplayName = document.getElementById('selected-model').textC
"openai/gpt-4-32k": "GPT-4-32k is an extended version of GPT-4, with the same capabilities but quadrupled context length, allowing for processing up to 40 pages of text in a single pass. This is particularly beneficial for handling longer content like interacting with PDFs without an external vector database. Training data: up to Sep 2021.",
"openai/gpt-3.5-turbo-16k": "This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up to Sep 2021.",
"nousresearch/nous-hermes-llama2-13b": "A state-of-the-art language model fine-tuned on over 300k instructions by Nous Research, with Teknium and Emozilla leading the fine tuning process.",
- "phind/phind-codellama-34b": "A fine-tune of CodeLlama-34B on an internal dataset that helps it exceed GPT-4 on some benchmarks, including HumanEval.",
- "meta-llama/codellama-34b-instruct": "Code Llama is built upon Llama 2 and excels at filling in code, handling extensive input contexts, and following programming instructions without prior training for various programming tasks.",
+ "huggingfaceh4/zephyr-7b-beta:free": "Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-\u03b2 is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](/mistralai/mistral-7b-instruct-v0.1) that was trained on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO).\n\n_These are free, rate-limited endpoints for [Zephyr 7B](/huggingfaceh4/zephyr-7b-beta). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"mancer/weaver": "An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.",
- "anthropic/claude-instant-1:beta": "This is a lower-latency version of [Claude Instant v1](/models/anthropic/claude-instant-1), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nAnthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
- "anthropic/claude-2.0:beta": "This is a lower-latency version of [Claude v2.0](/models/anthropic/claude-2.0), made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the model's side instead of OpenRouter's. It's in beta, and may change in the future.\n\nAnthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.",
"anthropic/claude-instant-1.0": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
"anthropic/claude-1.2": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
"anthropic/claude-1": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
"anthropic/claude-instant-1": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.",
+ "anthropic/claude-instant-1:beta": "Anthropic's model for low-latency, high throughput text generation. Supports hundreds of pages of text.\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-instant-1) variant._",
"anthropic/claude-2.0": "Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.",
+ "anthropic/claude-2.0:beta": "Anthropic's flagship model. Superior performance on tasks that require complex reasoning. Supports hundreds of pages of text.\n\n_This is a faster endpoint, made available in collaboration with Anthropic, that is self-moderated: response moderation happens on the provider's side instead of OpenRouter's. For requests that pass moderation, it's identical to the [Standard](/anthropic/claude-2.0) variant._",
"undi95/remm-slerp-l2-13b": "A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge",
+ "undi95/remm-slerp-l2-13b:extended": "A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge\n\n_These are extended-context endpoints for [ReMM SLERP 13B](/undi95/remm-slerp-l2-13b). They may have higher prices._",
"google/palm-2-codechat-bison": "PaLM 2 fine-tuned for chatbot conversations that help with code-related questions.",
"google/palm-2-chat-bison": "PaLM 2 is a language model by Google with improved multilingual, reasoning and coding capabilities.",
+ "gryphe/mythomax-l2-13b:free": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge\n\n_These are free, rate-limited endpoints for [MythoMax 13B](/gryphe/mythomax-l2-13b). Outputs may be cached. Read about rate limits [here](/docs/limits)._",
"gryphe/mythomax-l2-13b": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge",
- "meta-llama/llama-2-70b-chat": "The flagship, 70 billion parameter language model from Meta, fine tuned for chat completions. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.",
+ "gryphe/mythomax-l2-13b:nitro": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge\n\n_These are higher-throughput endpoints for [MythoMax 13B](/gryphe/mythomax-l2-13b). They may have higher prices._",
+ "gryphe/mythomax-l2-13b:extended": "One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge\n\n_These are extended-context endpoints for [MythoMax 13B](/gryphe/mythomax-l2-13b). They may have higher prices._",
"meta-llama/llama-2-13b-chat": "A 13 billion parameter language model from Meta, fine tuned for chat completions",
"openai/gpt-4-0314": "GPT-4-0314 is the first version of GPT-4 released, with a context length of 8,192 tokens, and was supported until June 14. Training data: up to Sep 2021.",
"openai/gpt-4": "OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning capabilities. Training data: up to Sep 2021.",
- "openai/gpt-3.5-turbo-0301": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.",
"openai/gpt-3.5-turbo-0125": "The latest GPT-3.5 Turbo model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Sep 2021.\n\nThis version has a higher accuracy at responding in requested formats and a fix for a bug which caused a text encoding issue for non-English language function calls.",
"openai/gpt-3.5-turbo": "GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021."
};
@@ -1030,155 +1090,174 @@ document.getElementById('model-claude-3.5-sonnet').addEventListener('mouseover',
// select open router models lol
// Event listeners for additional models
+document.getElementById('open-router-model-neversleep-llama-3.1-lumimaid-70b').addEventListener('click', () => selectModel('neversleep/llama-3.1-lumimaid-70b'));
+document.getElementById('open-router-model-anthracite-org-magnum-v4-72b').addEventListener('click', () => selectModel('anthracite-org/magnum-v4-72b'));
+document.getElementById('open-router-model-anthropic-claude-3.5-sonnet').addEventListener('click', () => selectModel('anthropic/claude-3.5-sonnet'));
+document.getElementById('open-router-model-anthropic-claude-3.5-sonnet-beta').addEventListener('click', () => selectModel('anthropic/claude-3.5-sonnet:beta'));
+document.getElementById('open-router-model-x-ai-grok-beta').addEventListener('click', () => selectModel('x-ai/grok-beta'));
+document.getElementById('open-router-model-mistralai-ministral-8b').addEventListener('click', () => selectModel('mistralai/ministral-8b'));
+document.getElementById('open-router-model-mistralai-ministral-3b').addEventListener('click', () => selectModel('mistralai/ministral-3b'));
+document.getElementById('open-router-model-qwen-qwen-2.5-7b-instruct').addEventListener('click', () => selectModel('qwen/qwen-2.5-7b-instruct'));
+document.getElementById('open-router-model-nvidia-llama-3.1-nemotron-70b-instruct').addEventListener('click', () => selectModel('nvidia/llama-3.1-nemotron-70b-instruct'));
+document.getElementById('open-router-model-inflection-inflection-3-pi').addEventListener('click', () => selectModel('inflection/inflection-3-pi'));
+document.getElementById('open-router-model-inflection-inflection-3-productivity').addEventListener('click', () => selectModel('inflection/inflection-3-productivity'));
+document.getElementById('open-router-model-google-gemini-flash-1.5-8b').addEventListener('click', () => selectModel('google/gemini-flash-1.5-8b'));
+document.getElementById('open-router-model-liquid-lfm-40b-free').addEventListener('click', () => selectModel('liquid/lfm-40b:free'));
+document.getElementById('open-router-model-liquid-lfm-40b').addEventListener('click', () => selectModel('liquid/lfm-40b'));
+document.getElementById('open-router-model-thedrummer-rocinante-12b').addEventListener('click', () => selectModel('thedrummer/rocinante-12b'));
+document.getElementById('open-router-model-eva-unit-01-eva-qwen-2.5-14b').addEventListener('click', () => selectModel('eva-unit-01/eva-qwen-2.5-14b'));
+document.getElementById('open-router-model-anthracite-org-magnum-v2-72b').addEventListener('click', () => selectModel('anthracite-org/magnum-v2-72b'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-3b-instruct-free').addEventListener('click', () => selectModel('meta-llama/llama-3.2-3b-instruct:free'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-3b-instruct').addEventListener('click', () => selectModel('meta-llama/llama-3.2-3b-instruct'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-1b-instruct-free').addEventListener('click', () => selectModel('meta-llama/llama-3.2-1b-instruct:free'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-1b-instruct').addEventListener('click', () => selectModel('meta-llama/llama-3.2-1b-instruct'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-90b-vision-instruct').addEventListener('click', () => selectModel('meta-llama/llama-3.2-90b-vision-instruct'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-11b-vision-instruct-free').addEventListener('click', () => selectModel('meta-llama/llama-3.2-11b-vision-instruct:free'));
+document.getElementById('open-router-model-meta-llama-llama-3.2-11b-vision-instruct').addEventListener('click', () => selectModel('meta-llama/llama-3.2-11b-vision-instruct'));
+document.getElementById('open-router-model-qwen-qwen-2.5-72b-instruct').addEventListener('click', () => selectModel('qwen/qwen-2.5-72b-instruct'));
+document.getElementById('open-router-model-qwen-qwen-2-vl-72b-instruct').addEventListener('click', () => selectModel('qwen/qwen-2-vl-72b-instruct'));
+document.getElementById('open-router-model-neversleep-llama-3.1-lumimaid-8b').addEventListener('click', () => selectModel('neversleep/llama-3.1-lumimaid-8b'));
+document.getElementById('open-router-model-openai-o1-mini-2024-09-12').addEventListener('click', () => selectModel('openai/o1-mini-2024-09-12'));
+document.getElementById('open-router-model-openai-o1-mini').addEventListener('click', () => selectModel('openai/o1-mini'));
+document.getElementById('open-router-model-openai-o1-preview-2024-09-12').addEventListener('click', () => selectModel('openai/o1-preview-2024-09-12'));
+document.getElementById('open-router-model-openai-o1-preview').addEventListener('click', () => selectModel('openai/o1-preview'));
+document.getElementById('open-router-model-mistralai-pixtral-12b').addEventListener('click', () => selectModel('mistralai/pixtral-12b'));
+document.getElementById('open-router-model-cohere-command-r-plus-08-2024').addEventListener('click', () => selectModel('cohere/command-r-plus-08-2024'));
+document.getElementById('open-router-model-cohere-command-r-08-2024').addEventListener('click', () => selectModel('cohere/command-r-08-2024'));
+document.getElementById('open-router-model-qwen-qwen-2-vl-7b-instruct').addEventListener('click', () => selectModel('qwen/qwen-2-vl-7b-instruct'));
+document.getElementById('open-router-model-google-gemini-flash-1.5-8b-exp').addEventListener('click', () => selectModel('google/gemini-flash-1.5-8b-exp'));
+document.getElementById('open-router-model-sao10k-l3.1-euryale-70b').addEventListener('click', () => selectModel('sao10k/l3.1-euryale-70b'));
+document.getElementById('open-router-model-google-gemini-flash-1.5-exp').addEventListener('click', () => selectModel('google/gemini-flash-1.5-exp'));
+document.getElementById('open-router-model-ai21-jamba-1-5-large').addEventListener('click', () => selectModel('ai21/jamba-1-5-large'));
+document.getElementById('open-router-model-ai21-jamba-1-5-mini').addEventListener('click', () => selectModel('ai21/jamba-1-5-mini'));
+document.getElementById('open-router-model-microsoft-phi-3.5-mini-128k-instruct').addEventListener('click', () => selectModel('microsoft/phi-3.5-mini-128k-instruct'));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-70b').addEventListener('click', () => selectModel('nousresearch/hermes-3-llama-3.1-70b'));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b-free').addEventListener('click', () => selectModel('nousresearch/hermes-3-llama-3.1-405b:free'));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b').addEventListener('click', () => selectModel('nousresearch/hermes-3-llama-3.1-405b'));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b-extended').addEventListener('click', () => selectModel('nousresearch/hermes-3-llama-3.1-405b:extended'));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-huge-128k-online').addEventListener('click', () => selectModel('perplexity/llama-3.1-sonar-huge-128k-online'));
+document.getElementById('open-router-model-openai-chatgpt-4o-latest').addEventListener('click', () => selectModel('openai/chatgpt-4o-latest'));
+document.getElementById('open-router-model-sao10k-l3-lunaris-8b').addEventListener('click', () => selectModel('sao10k/l3-lunaris-8b'));
+document.getElementById('open-router-model-aetherwiing-mn-starcannon-12b').addEventListener('click', () => selectModel('aetherwiing/mn-starcannon-12b'));
+document.getElementById('open-router-model-openai-gpt-4o-2024-08-06').addEventListener('click', () => selectModel('openai/gpt-4o-2024-08-06'));
+document.getElementById('open-router-model-meta-llama-llama-3.1-405b').addEventListener('click', () => selectModel('meta-llama/llama-3.1-405b'));
+document.getElementById('open-router-model-nothingiisreal-mn-celeste-12b').addEventListener('click', () => selectModel('nothingiisreal/mn-celeste-12b'));
+document.getElementById('open-router-model-google-gemini-pro-1.5-exp').addEventListener('click', () => selectModel('google/gemini-pro-1.5-exp'));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-large-128k-online').addEventListener('click', () => selectModel('perplexity/llama-3.1-sonar-large-128k-online'));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-large-128k-chat').addEventListener('click', () => selectModel('perplexity/llama-3.1-sonar-large-128k-chat'));
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document.getElementById('open-router-model-openai-gpt-4-vision-preview').addEventListener('click', () => selectModel('openai/gpt-4-vision-preview'));
document.getElementById('open-router-model-lizpreciatior-lzlv-70b-fp16-hf').addEventListener('click', () => selectModel('lizpreciatior/lzlv-70b-fp16-hf'));
-document.getElementById('open-router-model-undi95-toppy-m-7b-free').addEventListener('click', () => selectModel('undi95/toppy-m-7b:free'));
document.getElementById('open-router-model-alpindale-goliath-120b').addEventListener('click', () => selectModel('alpindale/goliath-120b'));
+document.getElementById('open-router-model-undi95-toppy-m-7b-free').addEventListener('click', () => selectModel('undi95/toppy-m-7b:free'));
document.getElementById('open-router-model-undi95-toppy-m-7b').addEventListener('click', () => selectModel('undi95/toppy-m-7b'));
+document.getElementById('open-router-model-undi95-toppy-m-7b-nitro').addEventListener('click', () => selectModel('undi95/toppy-m-7b:nitro'));
document.getElementById('open-router-model-openrouter-auto').addEventListener('click', () => selectModel('openrouter/auto'));
document.getElementById('open-router-model-openai-gpt-4-1106-preview').addEventListener('click', () => selectModel('openai/gpt-4-1106-preview'));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-1106').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo-1106'));
-document.getElementById('open-router-model-huggingfaceh4-zephyr-7b-beta-free').addEventListener('click', () => selectModel('huggingfaceh4/zephyr-7b-beta:free'));
document.getElementById('open-router-model-google-palm-2-codechat-bison-32k').addEventListener('click', () => selectModel('google/palm-2-codechat-bison-32k'));
document.getElementById('open-router-model-google-palm-2-chat-bison-32k').addEventListener('click', () => selectModel('google/palm-2-chat-bison-32k'));
-document.getElementById('open-router-model-teknium-openhermes-2-mistral-7b').addEventListener('click', () => selectModel('teknium/openhermes-2-mistral-7b'));
-document.getElementById('open-router-model-open-orca-mistral-7b-openorca').addEventListener('click', () => selectModel('open-orca/mistral-7b-openorca'));
document.getElementById('open-router-model-jondurbin-airoboros-l2-70b').addEventListener('click', () => selectModel('jondurbin/airoboros-l2-70b'));
-document.getElementById('open-router-model-gryphe-mythomax-l2-13b-extended').addEventListener('click', () => selectModel('gryphe/mythomax-l2-13b:extended'));
document.getElementById('open-router-model-xwin-lm-xwin-lm-70b').addEventListener('click', () => selectModel('xwin-lm/xwin-lm-70b'));
-document.getElementById('open-router-model-mistralai-mistral-7b-instruct-free').addEventListener('click', () => selectModel('mistralai/mistral-7b-instruct:free'));
document.getElementById('open-router-model-mistralai-mistral-7b-instruct-v0.1').addEventListener('click', () => selectModel('mistralai/mistral-7b-instruct-v0.1'));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-instruct').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo-instruct'));
document.getElementById('open-router-model-pygmalionai-mythalion-13b').addEventListener('click', () => selectModel('pygmalionai/mythalion-13b'));
@@ -1186,178 +1265,198 @@ document.getElementById('open-router-model-openai-gpt-4-32k-0314').addEventListe
document.getElementById('open-router-model-openai-gpt-4-32k').addEventListener('click', () => selectModel('openai/gpt-4-32k'));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-16k').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo-16k'));
document.getElementById('open-router-model-nousresearch-nous-hermes-llama2-13b').addEventListener('click', () => selectModel('nousresearch/nous-hermes-llama2-13b'));
-document.getElementById('open-router-model-phind-phind-codellama-34b').addEventListener('click', () => selectModel('phind/phind-codellama-34b'));
-document.getElementById('open-router-model-meta-llama-codellama-34b-instruct').addEventListener('click', () => selectModel('meta-llama/codellama-34b-instruct'));
+document.getElementById('open-router-model-huggingfaceh4-zephyr-7b-beta-free').addEventListener('click', () => selectModel('huggingfaceh4/zephyr-7b-beta:free'));
document.getElementById('open-router-model-mancer-weaver').addEventListener('click', () => selectModel('mancer/weaver'));
-document.getElementById('open-router-model-anthropic-claude-instant-1-beta').addEventListener('click', () => selectModel('anthropic/claude-instant-1:beta'));
-document.getElementById('open-router-model-anthropic-claude-2.0-beta').addEventListener('click', () => selectModel('anthropic/claude-2.0:beta'));
document.getElementById('open-router-model-anthropic-claude-instant-1.0').addEventListener('click', () => selectModel('anthropic/claude-instant-1.0'));
document.getElementById('open-router-model-anthropic-claude-1.2').addEventListener('click', () => selectModel('anthropic/claude-1.2'));
document.getElementById('open-router-model-anthropic-claude-1').addEventListener('click', () => selectModel('anthropic/claude-1'));
document.getElementById('open-router-model-anthropic-claude-instant-1').addEventListener('click', () => selectModel('anthropic/claude-instant-1'));
+document.getElementById('open-router-model-anthropic-claude-instant-1-beta').addEventListener('click', () => selectModel('anthropic/claude-instant-1:beta'));
document.getElementById('open-router-model-anthropic-claude-2.0').addEventListener('click', () => selectModel('anthropic/claude-2.0'));
+document.getElementById('open-router-model-anthropic-claude-2.0-beta').addEventListener('click', () => selectModel('anthropic/claude-2.0:beta'));
document.getElementById('open-router-model-undi95-remm-slerp-l2-13b').addEventListener('click', () => selectModel('undi95/remm-slerp-l2-13b'));
+document.getElementById('open-router-model-undi95-remm-slerp-l2-13b-extended').addEventListener('click', () => selectModel('undi95/remm-slerp-l2-13b:extended'));
document.getElementById('open-router-model-google-palm-2-codechat-bison').addEventListener('click', () => selectModel('google/palm-2-codechat-bison'));
document.getElementById('open-router-model-google-palm-2-chat-bison').addEventListener('click', () => selectModel('google/palm-2-chat-bison'));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-free').addEventListener('click', () => selectModel('gryphe/mythomax-l2-13b:free'));
document.getElementById('open-router-model-gryphe-mythomax-l2-13b').addEventListener('click', () => selectModel('gryphe/mythomax-l2-13b'));
-document.getElementById('open-router-model-meta-llama-llama-2-70b-chat').addEventListener('click', () => selectModel('meta-llama/llama-2-70b-chat'));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-nitro').addEventListener('click', () => selectModel('gryphe/mythomax-l2-13b:nitro'));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-extended').addEventListener('click', () => selectModel('gryphe/mythomax-l2-13b:extended'));
document.getElementById('open-router-model-meta-llama-llama-2-13b-chat').addEventListener('click', () => selectModel('meta-llama/llama-2-13b-chat'));
document.getElementById('open-router-model-openai-gpt-4-0314').addEventListener('click', () => selectModel('openai/gpt-4-0314'));
document.getElementById('open-router-model-openai-gpt-4').addEventListener('click', () => selectModel('openai/gpt-4'));
-document.getElementById('open-router-model-openai-gpt-3.5-turbo-0301').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo-0301'));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-0125').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo-0125'));
document.getElementById('open-router-model-openai-gpt-3.5-turbo').addEventListener('click', () => selectModel('openai/gpt-3.5-turbo'));
// event listeners for descrptions
+document.getElementById('open-router-model-neversleep-llama-3.1-lumimaid-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['neversleep/llama-3.1-lumimaid-70b'], event.currentTarget));
+document.getElementById('open-router-model-anthracite-org-magnum-v4-72b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthracite-org/magnum-v4-72b'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-3.5-sonnet').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-3.5-sonnet'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-3.5-sonnet-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-3.5-sonnet:beta'], event.currentTarget));
+document.getElementById('open-router-model-x-ai-grok-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['x-ai/grok-beta'], event.currentTarget));
+document.getElementById('open-router-model-mistralai-ministral-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/ministral-8b'], event.currentTarget));
+document.getElementById('open-router-model-mistralai-ministral-3b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/ministral-3b'], event.currentTarget));
+document.getElementById('open-router-model-qwen-qwen-2.5-7b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2.5-7b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-nvidia-llama-3.1-nemotron-70b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nvidia/llama-3.1-nemotron-70b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-inflection-inflection-3-pi').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['inflection/inflection-3-pi'], event.currentTarget));
+document.getElementById('open-router-model-inflection-inflection-3-productivity').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['inflection/inflection-3-productivity'], event.currentTarget));
+document.getElementById('open-router-model-google-gemini-flash-1.5-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemini-flash-1.5-8b'], event.currentTarget));
+document.getElementById('open-router-model-liquid-lfm-40b-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['liquid/lfm-40b:free'], event.currentTarget));
+document.getElementById('open-router-model-liquid-lfm-40b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['liquid/lfm-40b'], event.currentTarget));
+document.getElementById('open-router-model-thedrummer-rocinante-12b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['thedrummer/rocinante-12b'], event.currentTarget));
+document.getElementById('open-router-model-eva-unit-01-eva-qwen-2.5-14b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['eva-unit-01/eva-qwen-2.5-14b'], event.currentTarget));
+document.getElementById('open-router-model-anthracite-org-magnum-v2-72b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthracite-org/magnum-v2-72b'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-3b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-3b-instruct:free'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-3b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-3b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-1b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-1b-instruct:free'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-1b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-1b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-90b-vision-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-90b-vision-instruct'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-11b-vision-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-11b-vision-instruct:free'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.2-11b-vision-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.2-11b-vision-instruct'], event.currentTarget));
+document.getElementById('open-router-model-qwen-qwen-2.5-72b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2.5-72b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-qwen-qwen-2-vl-72b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2-vl-72b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-neversleep-llama-3.1-lumimaid-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['neversleep/llama-3.1-lumimaid-8b'], event.currentTarget));
+document.getElementById('open-router-model-openai-o1-mini-2024-09-12').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/o1-mini-2024-09-12'], event.currentTarget));
+document.getElementById('open-router-model-openai-o1-mini').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/o1-mini'], event.currentTarget));
+document.getElementById('open-router-model-openai-o1-preview-2024-09-12').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/o1-preview-2024-09-12'], event.currentTarget));
+document.getElementById('open-router-model-openai-o1-preview').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/o1-preview'], event.currentTarget));
+document.getElementById('open-router-model-mistralai-pixtral-12b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/pixtral-12b'], event.currentTarget));
+document.getElementById('open-router-model-cohere-command-r-plus-08-2024').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['cohere/command-r-plus-08-2024'], event.currentTarget));
+document.getElementById('open-router-model-cohere-command-r-08-2024').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['cohere/command-r-08-2024'], event.currentTarget));
+document.getElementById('open-router-model-qwen-qwen-2-vl-7b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2-vl-7b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-google-gemini-flash-1.5-8b-exp').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemini-flash-1.5-8b-exp'], event.currentTarget));
+document.getElementById('open-router-model-sao10k-l3.1-euryale-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['sao10k/l3.1-euryale-70b'], event.currentTarget));
+document.getElementById('open-router-model-google-gemini-flash-1.5-exp').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemini-flash-1.5-exp'], event.currentTarget));
+document.getElementById('open-router-model-ai21-jamba-1-5-large').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['ai21/jamba-1-5-large'], event.currentTarget));
+document.getElementById('open-router-model-ai21-jamba-1-5-mini').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['ai21/jamba-1-5-mini'], event.currentTarget));
+document.getElementById('open-router-model-microsoft-phi-3.5-mini-128k-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['microsoft/phi-3.5-mini-128k-instruct'], event.currentTarget));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/hermes-3-llama-3.1-70b'], event.currentTarget));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/hermes-3-llama-3.1-405b:free'], event.currentTarget));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/hermes-3-llama-3.1-405b'], event.currentTarget));
+document.getElementById('open-router-model-nousresearch-hermes-3-llama-3.1-405b-extended').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/hermes-3-llama-3.1-405b:extended'], event.currentTarget));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-huge-128k-online').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['perplexity/llama-3.1-sonar-huge-128k-online'], event.currentTarget));
+document.getElementById('open-router-model-openai-chatgpt-4o-latest').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/chatgpt-4o-latest'], event.currentTarget));
+document.getElementById('open-router-model-sao10k-l3-lunaris-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['sao10k/l3-lunaris-8b'], event.currentTarget));
+document.getElementById('open-router-model-aetherwiing-mn-starcannon-12b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['aetherwiing/mn-starcannon-12b'], event.currentTarget));
+document.getElementById('open-router-model-openai-gpt-4o-2024-08-06').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4o-2024-08-06'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-405b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-405b'], event.currentTarget));
+document.getElementById('open-router-model-nothingiisreal-mn-celeste-12b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nothingiisreal/mn-celeste-12b'], event.currentTarget));
+document.getElementById('open-router-model-google-gemini-pro-1.5-exp').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemini-pro-1.5-exp'], event.currentTarget));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-large-128k-online').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['perplexity/llama-3.1-sonar-large-128k-online'], event.currentTarget));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-large-128k-chat').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['perplexity/llama-3.1-sonar-large-128k-chat'], event.currentTarget));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-small-128k-online').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['perplexity/llama-3.1-sonar-small-128k-online'], event.currentTarget));
+document.getElementById('open-router-model-perplexity-llama-3.1-sonar-small-128k-chat').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['perplexity/llama-3.1-sonar-small-128k-chat'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-70b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-70b-instruct:free'], event.currentTarget));
document.getElementById('open-router-model-meta-llama-llama-3.1-70b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-70b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-70b-instruct-nitro').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-70b-instruct:nitro'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-8b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-8b-instruct:free'], event.currentTarget));
document.getElementById('open-router-model-meta-llama-llama-3.1-8b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-8b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-405b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-405b-instruct:free'], event.currentTarget));
document.getElementById('open-router-model-meta-llama-llama-3.1-405b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-405b-instruct'], event.currentTarget));
-document.getElementById('open-router-model-cognitivecomputations-dolphin-llama-3-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['cognitivecomputations/dolphin-llama-3-70b'], event.currentTarget));
+document.getElementById('open-router-model-meta-llama-llama-3.1-405b-instruct-nitro').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-3.1-405b-instruct:nitro'], event.currentTarget));
document.getElementById('open-router-model-mistralai-codestral-mamba').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/codestral-mamba'], event.currentTarget));
document.getElementById('open-router-model-mistralai-mistral-nemo').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/mistral-nemo'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4o-mini-2024-07-18').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4o-mini-2024-07-18'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4o-mini').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4o-mini'], event.currentTarget));
+document.getElementById('open-router-model-qwen-qwen-2-7b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2-7b-instruct:free'], event.currentTarget));
document.getElementById('open-router-model-qwen-qwen-2-7b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['qwen/qwen-2-7b-instruct'], event.currentTarget));
document.getElementById('open-router-model-google-gemma-2-27b-it').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemma-2-27b-it'], event.currentTarget));
document.getElementById('open-router-model-alpindale-magnum-72b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['alpindale/magnum-72b'], event.currentTarget));
document.getElementById('open-router-model-nousresearch-hermes-2-theta-llama-3-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/hermes-2-theta-llama-3-8b'], event.currentTarget));
document.getElementById('open-router-model-google-gemma-2-9b-it-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemma-2-9b-it:free'], event.currentTarget));
document.getElementById('open-router-model-google-gemma-2-9b-it').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/gemma-2-9b-it'], event.currentTarget));
-document.getElementById('open-router-model-openrouter-flavor-of-the-week').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openrouter/flavor-of-the-week'], event.currentTarget));
-document.getElementById('open-router-model-sao10k-l3-stheno-8b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['sao10k/l3-stheno-8b'], event.currentTarget));
document.getElementById('open-router-model-ai21-jamba-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['ai21/jamba-instruct'], event.currentTarget));
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-document.getElementById('open-router-model-anthropic-claude-2.1-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.1:beta'], event.currentTarget));
-document.getElementById('open-router-model-anthropic-claude-2-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2:beta'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-instant-1.1').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-instant-1.1'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-2.1').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.1'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-2.1-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.1:beta'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-2').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-2-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2:beta'], event.currentTarget));
document.getElementById('open-router-model-teknium-openhermes-2.5-mistral-7b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['teknium/openhermes-2.5-mistral-7b'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4-vision-preview').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4-vision-preview'], event.currentTarget));
document.getElementById('open-router-model-lizpreciatior-lzlv-70b-fp16-hf').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['lizpreciatior/lzlv-70b-fp16-hf'], event.currentTarget));
-document.getElementById('open-router-model-undi95-toppy-m-7b-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/toppy-m-7b:free'], event.currentTarget));
document.getElementById('open-router-model-alpindale-goliath-120b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['alpindale/goliath-120b'], event.currentTarget));
+document.getElementById('open-router-model-undi95-toppy-m-7b-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/toppy-m-7b:free'], event.currentTarget));
document.getElementById('open-router-model-undi95-toppy-m-7b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/toppy-m-7b'], event.currentTarget));
+document.getElementById('open-router-model-undi95-toppy-m-7b-nitro').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/toppy-m-7b:nitro'], event.currentTarget));
document.getElementById('open-router-model-openrouter-auto').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openrouter/auto'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4-1106-preview').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4-1106-preview'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-1106').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo-1106'], event.currentTarget));
-document.getElementById('open-router-model-huggingfaceh4-zephyr-7b-beta-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['huggingfaceh4/zephyr-7b-beta:free'], event.currentTarget));
document.getElementById('open-router-model-google-palm-2-codechat-bison-32k').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/palm-2-codechat-bison-32k'], event.currentTarget));
document.getElementById('open-router-model-google-palm-2-chat-bison-32k').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/palm-2-chat-bison-32k'], event.currentTarget));
-document.getElementById('open-router-model-teknium-openhermes-2-mistral-7b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['teknium/openhermes-2-mistral-7b'], event.currentTarget));
-document.getElementById('open-router-model-open-orca-mistral-7b-openorca').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['open-orca/mistral-7b-openorca'], event.currentTarget));
document.getElementById('open-router-model-jondurbin-airoboros-l2-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['jondurbin/airoboros-l2-70b'], event.currentTarget));
-document.getElementById('open-router-model-gryphe-mythomax-l2-13b-extended').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['gryphe/mythomax-l2-13b:extended'], event.currentTarget));
document.getElementById('open-router-model-xwin-lm-xwin-lm-70b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['xwin-lm/xwin-lm-70b'], event.currentTarget));
-document.getElementById('open-router-model-mistralai-mistral-7b-instruct-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/mistral-7b-instruct:free'], event.currentTarget));
document.getElementById('open-router-model-mistralai-mistral-7b-instruct-v0.1').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mistralai/mistral-7b-instruct-v0.1'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo-instruct'], event.currentTarget));
document.getElementById('open-router-model-pygmalionai-mythalion-13b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['pygmalionai/mythalion-13b'], event.currentTarget));
@@ -1365,30 +1464,30 @@ document.getElementById('open-router-model-openai-gpt-4-32k-0314').addEventListe
document.getElementById('open-router-model-openai-gpt-4-32k').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4-32k'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-16k').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo-16k'], event.currentTarget));
document.getElementById('open-router-model-nousresearch-nous-hermes-llama2-13b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['nousresearch/nous-hermes-llama2-13b'], event.currentTarget));
-document.getElementById('open-router-model-phind-phind-codellama-34b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['phind/phind-codellama-34b'], event.currentTarget));
-document.getElementById('open-router-model-meta-llama-codellama-34b-instruct').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/codellama-34b-instruct'], event.currentTarget));
+document.getElementById('open-router-model-huggingfaceh4-zephyr-7b-beta-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['huggingfaceh4/zephyr-7b-beta:free'], event.currentTarget));
document.getElementById('open-router-model-mancer-weaver').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['mancer/weaver'], event.currentTarget));
-document.getElementById('open-router-model-anthropic-claude-instant-1-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-instant-1:beta'], event.currentTarget));
-document.getElementById('open-router-model-anthropic-claude-2.0-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.0:beta'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-instant-1.0').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-instant-1.0'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-1.2').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-1.2'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-1').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-1'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-instant-1').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-instant-1'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-instant-1-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-instant-1:beta'], event.currentTarget));
document.getElementById('open-router-model-anthropic-claude-2.0').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.0'], event.currentTarget));
+document.getElementById('open-router-model-anthropic-claude-2.0-beta').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['anthropic/claude-2.0:beta'], event.currentTarget));
document.getElementById('open-router-model-undi95-remm-slerp-l2-13b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/remm-slerp-l2-13b'], event.currentTarget));
+document.getElementById('open-router-model-undi95-remm-slerp-l2-13b-extended').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['undi95/remm-slerp-l2-13b:extended'], event.currentTarget));
document.getElementById('open-router-model-google-palm-2-codechat-bison').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/palm-2-codechat-bison'], event.currentTarget));
document.getElementById('open-router-model-google-palm-2-chat-bison').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['google/palm-2-chat-bison'], event.currentTarget));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-free').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['gryphe/mythomax-l2-13b:free'], event.currentTarget));
document.getElementById('open-router-model-gryphe-mythomax-l2-13b').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['gryphe/mythomax-l2-13b'], event.currentTarget));
-document.getElementById('open-router-model-meta-llama-llama-2-70b-chat').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-2-70b-chat'], event.currentTarget));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-nitro').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['gryphe/mythomax-l2-13b:nitro'], event.currentTarget));
+document.getElementById('open-router-model-gryphe-mythomax-l2-13b-extended').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['gryphe/mythomax-l2-13b:extended'], event.currentTarget));
document.getElementById('open-router-model-meta-llama-llama-2-13b-chat').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['meta-llama/llama-2-13b-chat'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4-0314').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4-0314'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-4').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-4'], event.currentTarget));
-document.getElementById('open-router-model-openai-gpt-3.5-turbo-0301').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo-0301'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-3.5-turbo-0125').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo-0125'], event.currentTarget));
document.getElementById('open-router-model-openai-gpt-3.5-turbo').addEventListener('mouseover', (event) => showCustomTooltip(modelDescriptions['openai/gpt-3.5-turbo'], event.currentTarget));
-
// Add mouseout event listener for all model buttons
document.querySelectorAll('.select-options button').forEach(button => {
button.addEventListener('mouseout', hideCustomTooltip);