diff --git a/.changeset/orange-peaches-buy.md b/.changeset/orange-peaches-buy.md
new file mode 100644
index 000000000000..7c4939f1d664
--- /dev/null
+++ b/.changeset/orange-peaches-buy.md
@@ -0,0 +1,5 @@
+---
+'@ai-sdk/azure': patch
+---
+
+Support idempotent doGenerate/doStream with pre-supplied result chunks in Responses API tests.
diff --git a/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.chunks.txt b/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.chunks.txt
new file mode 100644
index 000000000000..2e2a48d6a168
--- /dev/null
+++ b/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.chunks.txt
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+{"type":"response.output_text.done","sequence_number":337,"item_id":"msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6","output_index":2,"content_index":0,"text":"Here's a simple Python program that generates five random numbers between 1 and 100 with two decimal places, and the execution result from running it.\n\nProgram:\n```python\nimport random\n\n# Generate five random numbers and round to two decimals\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the list and a nicely formatted string\nprint(\"Generated numbers (as floats):\", numbers)\nprint(\"Generated numbers (formatted to 2 decimal places):\", \", \".join(f\"{n:.2f}\" for n in numbers))\n```\n\nExecution result (from this run):\nGenerated numbers (as floats): [88.99, 51.21, 89.85, 88.97, 7.73]\nGenerated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73\n\nIf you want a reproducible result, I can add a fixed random seed or save the program to a file for you. Want that?","logprobs":[]}
+{"type":"response.content_part.done","sequence_number":338,"item_id":"msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6","output_index":2,"content_index":0,"part":{"type":"output_text","annotations":[],"logprobs":[],"text":"Here's a simple Python program that generates five random numbers between 1 and 100 with two decimal places, and the execution result from running it.\n\nProgram:\n```python\nimport random\n\n# Generate five random numbers and round to two decimals\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the list and a nicely formatted string\nprint(\"Generated numbers (as floats):\", numbers)\nprint(\"Generated numbers (formatted to 2 decimal places):\", \", \".join(f\"{n:.2f}\" for n in numbers))\n```\n\nExecution result (from this run):\nGenerated numbers (as floats): [88.99, 51.21, 89.85, 88.97, 7.73]\nGenerated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73\n\nIf you want a reproducible result, I can add a fixed random seed or save the program to a file for you. Want that?"}}
+{"type":"response.output_item.done","sequence_number":339,"output_index":2,"item":{"id":"msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6","type":"message","status":"completed","content":[{"type":"output_text","annotations":[],"logprobs":[],"text":"Here's a simple Python program that generates five random numbers between 1 and 100 with two decimal places, and the execution result from running it.\n\nProgram:\n```python\nimport random\n\n# Generate five random numbers and round to two decimals\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the list and a nicely formatted string\nprint(\"Generated numbers (as floats):\", numbers)\nprint(\"Generated numbers (formatted to 2 decimal places):\", \", \".join(f\"{n:.2f}\" for n in numbers))\n```\n\nExecution result (from this run):\nGenerated numbers (as floats): [88.99, 51.21, 89.85, 88.97, 7.73]\nGenerated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73\n\nIf you want a reproducible result, I can add a fixed random seed or save the program to a file for you. Want that?"}],"role":"assistant"}}
+{"type":"response.completed","sequence_number":340,"response":{"id":"resp_0ad69c3c5fcb01f60068eba78cacd08193bf9a57724aa55ab8","object":"response","created_at":1760274316,"status":"completed","background":false,"content_filters":null,"error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"max_tool_calls":null,"model":"gpt-5-mini","output":[{"id":"rs_0ad69c3c5fcb01f60068eba78f12588193872253fcce2ef954","type":"reasoning","summary":[]},{"id":"ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f","type":"code_interpreter_call","status":"completed","code":"# Program: generate five random numbers between 1 and 100 with two decimal places\nimport random\n\n# Generate five random numbers and round to two decimals\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the list and a nicely formatted string\nprint(\"Generated numbers (as floats):\", numbers)\nprint(\"Generated numbers (formatted to 2 decimal places):\", \", \".join(f\"{n:.2f}\" for n in numbers))","container_id":"cntr_68eba78d6dc081908116b7ad7a32f52d0050b146e60e47a9","outputs":[{"type":"logs","logs":"Generated numbers (as floats): [88.99, 51.21, 89.85, 88.97, 7.73]\nGenerated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73\n"}]},{"id":"msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6","type":"message","status":"completed","content":[{"type":"output_text","annotations":[],"logprobs":[],"text":"Here's a simple Python program that generates five random numbers between 1 and 100 with two decimal places, and the execution result from running it.\n\nProgram:\n```python\nimport random\n\n# Generate five random numbers and round to two decimals\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the list and a nicely formatted string\nprint(\"Generated numbers (as floats):\", numbers)\nprint(\"Generated numbers (formatted to 2 decimal places):\", \", \".join(f\"{n:.2f}\" for n in numbers))\n```\n\nExecution result (from this run):\nGenerated numbers (as floats): [88.99, 51.21, 89.85, 88.97, 7.73]\nGenerated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73\n\nIf you want a reproducible result, I can add a fixed random seed or save the program to a file for you. Want that?"}],"role":"assistant"}],"parallel_tool_calls":true,"previous_response_id":null,"prompt_cache_key":null,"reasoning":{"effort":"medium","summary":null},"safety_identifier":null,"service_tier":"default","store":true,"temperature":1,"text":{"format":{"type":"text"},"verbosity":"medium"},"tool_choice":"auto","tools":[{"type":"code_interpreter","container":{"type":"auto"}}],"top_logprobs":0,"top_p":1,"truncation":"disabled","usage":{"input_tokens":2143,"input_tokens_details":{"cached_tokens":1280},"output_tokens":919,"output_tokens_details":{"reasoning_tokens":576},"total_tokens":3062},"user":null,"metadata":{}}}
diff --git a/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.json b/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.json
new file mode 100644
index 000000000000..453fb8d26196
--- /dev/null
+++ b/packages/azure/src/__fixtures__/azure-code-interpreter-tool.1.json
@@ -0,0 +1,90 @@
+{
+  "id": "resp_02ffbc38ae6fca1a0068ec87605cfc819793efdf27e5496675",
+  "object": "response",
+  "created_at": 1760331616,
+  "status": "completed",
+  "background": false,
+  "content_filters": null,
+  "error": null,
+  "incomplete_details": null,
+  "instructions": null,
+  "max_output_tokens": null,
+  "max_tool_calls": null,
+  "model": "gpt-5-mini",
+  "output": [
+    {
+      "id": "rs_02ffbc38ae6fca1a0068ec8762f61881979bf5191564930c23",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "ci_02ffbc38ae6fca1a0068ec876654a08197af4d648fe8bba210",
+      "type": "code_interpreter_call",
+      "status": "completed",
+      "code": "import random\n\n# Generate five random numbers between 1 and 100 with two decimal places\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\n\n# Print the program and its execution results\nprint(\"Program: Generate five random numbers between 1 and 100 with two decimal places\")\nprint(\"Generated numbers:\")\nfor i, num in enumerate(numbers, start=1):\n    print(f\"{i}: {num:.2f}\")",
+      "container_id": "cntr_68ec87613e58819097ec47888dc686d8088a1d37157e7565",
+      "outputs": [
+        {
+          "type": "logs",
+          "logs": "Program: Generate five random numbers between 1 and 100 with two decimal places\nGenerated numbers:\n1: 74.07\n2: 94.14\n3: 53.74\n4: 77.29\n5: 87.68\n"
+        }
+      ]
+    },
+    {
+      "id": "msg_02ffbc38ae6fca1a0068ec8768f35c81979428e19fa11468f1",
+      "type": "message",
+      "status": "completed",
+      "content": [
+        {
+          "type": "output_text",
+          "annotations": [],
+          "logprobs": [],
+          "text": "I ran the program and produced five random numbers between 1 and 100 with two decimal places. Results shown above:\n\n1: 74.07\n2: 94.14\n3: 53.74\n4: 77.29\n5: 87.68\n\nIf you want the code to be saved to a file, run multiple sets, or use a fixed seed for reproducible results, tell me and I can adjust."
+        }
+      ],
+      "role": "assistant"
+    }
+  ],
+  "parallel_tool_calls": true,
+  "previous_response_id": null,
+  "prompt_cache_key": null,
+  "reasoning": {
+    "effort": "medium",
+    "summary": null
+  },
+  "safety_identifier": null,
+  "service_tier": "default",
+  "store": true,
+  "temperature": 1,
+  "text": {
+    "format": {
+      "type": "text"
+    },
+    "verbosity": "medium"
+  },
+  "tool_choice": "auto",
+  "tools": [
+    {
+      "type": "code_interpreter",
+      "container": {
+        "type": "auto"
+      }
+    }
+  ],
+  "top_logprobs": 0,
+  "top_p": 1,
+  "truncation": "disabled",
+  "usage": {
+    "input_tokens": 1750,
+    "input_tokens_details": {
+      "cached_tokens": 0
+    },
+    "output_tokens": 400,
+    "output_tokens_details": {
+      "reasoning_tokens": 192
+    },
+    "total_tokens": 2150
+  },
+  "user": null,
+  "metadata": {}
+}
diff --git a/packages/azure/src/__fixtures__/openai-file-search-tool.1.chunks.txt b/packages/azure/src/__fixtures__/openai-file-search-tool.1.chunks.txt
new file mode 100644
index 000000000000..914a434eb071
--- /dev/null
+++ b/packages/azure/src/__fixtures__/openai-file-search-tool.1.chunks.txt
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+{"type":"response.content_part.done","sequence_number":90,"item_id":"msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f","output_index":3,"content_index":0,"part":{"type":"output_text","annotations":[{"type":"file_citation","file_id":"file-Ebzhf8H4DPGPr9pUhr7n7v","filename":"ai.pdf","index":379}],"logprobs":[],"text":"The document defines an embedding model as a tool that converts complex data (e.g., words or images) into a dense vector (a list of numbers) called an embedding. Unlike generative models, embedding models don’t generate new text or data; they provide vector representations that capture semantic and syntactic relationships and can be used as input for other models or NLP tasks ."}}
+{"type":"response.output_item.done","sequence_number":91,"output_index":3,"item":{"id":"msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f","type":"message","status":"completed","content":[{"type":"output_text","annotations":[{"type":"file_citation","file_id":"file-Ebzhf8H4DPGPr9pUhr7n7v","filename":"ai.pdf","index":379}],"logprobs":[],"text":"The document defines an embedding model as a tool that converts complex data (e.g., words or images) into a dense vector (a list of numbers) called an embedding. Unlike generative models, embedding models don’t generate new text or data; they provide vector representations that capture semantic and syntactic relationships and can be used as input for other models or NLP tasks ."}],"role":"assistant"}}
+{"type":"response.completed","sequence_number":92,"response":{"id":"resp_06456cb9918b63780068cacd710b0881a1b00b5fca56e7100b","object":"response","created_at":1758121329,"status":"completed","background":false,"error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"max_tool_calls":null,"model":"gpt-5-mini-2025-08-07","output":[{"id":"rs_06456cb9918b63780068cacd717c3481a1b8c7094da5770b41","type":"reasoning","summary":[]},{"id":"fs_06456cb9918b63780068cacd74a1dc81a1bf68dd57f140b4b6","type":"file_search_call","status":"completed","queries":["What is an embedding model according to this document?","What is an embedding model definition in this document?","How does the document define an embedding model?"],"results":[{"attributes":{},"file_id":"file-Ebzhf8H4DPGPr9pUhr7n7v","filename":"ai.pdf","score":0.9312,"text":"AI 1\n\nAI\nGenerative artificial intelligence refers to models that predict and generate \nvarious types of outputs (such as text, images, or audio) based on whatʼs \nstatistically likely, pulling from patterns theyʼve learned from their training data. \nFor example:\n\nGiven a photo, a generative model can generate a caption.\n\nGiven an audio file, a generative model can generate a transcription.\n\nGiven a text description, a generative model can generate an image.\n\nA large language model LLM is a subset of generative models focused \nprimarily on text. An LLM takes a sequence of words as input and aims to \npredict the most likely sequence to follow. It assigns probabilities to potential \nnext sequences and then selects one. The model continues to generate \nsequences until it meets a specified stopping criterion.\n\nLLMs learn by training on massive collections of written text, which means they \nwill be better suited to some use cases than others. For example, a model \ntrained on GitHub data would understand the probabilities of sequences in \nsource code particularly well.\n\nHowever, it's crucial to understand LLMs' limitations. When asked about less \nknown or absent information, like the birthday of a personal relative, LLMs \nmight \"hallucinate\" or make up information. It's essential to consider how well-\nrepresented the information you need is in the model.\n\nAn embedding model is used to convert complex data (like words or images) \ninto a dense vector (a list of numbers) representation, known as an embedding. \nUnlike generative models, embedding models do not generate new text or data. \nInstead, they provide representations of semantic and synactic relationships \nbetween entities that can be used as input for other models or other natural \nlanguage processing tasks.\n\nIn the next section, you will learn about the difference between models \nproviders and models, and which ones are available in the AI SDK."}]},{"id":"rs_06456cb9918b63780068cacd7663c481a191dd84333d842728","type":"reasoning","summary":[]},{"id":"msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f","type":"message","status":"completed","content":[{"type":"output_text","annotations":[{"type":"file_citation","file_id":"file-Ebzhf8H4DPGPr9pUhr7n7v","filename":"ai.pdf","index":379}],"logprobs":[],"text":"The document defines an embedding model as a tool that converts complex data (e.g., words or images) into a dense vector (a list of numbers) called an embedding. Unlike generative models, embedding models don’t generate new text or data; they provide vector representations that capture semantic and syntactic relationships and can be used as input for other models or NLP tasks ."}],"role":"assistant"}],"parallel_tool_calls":true,"previous_response_id":null,"prompt_cache_key":null,"reasoning":{"effort":"medium","summary":null},"safety_identifier":null,"service_tier":"default","store":true,"temperature":1,"text":{"format":{"type":"text"},"verbosity":"medium"},"tool_choice":"auto","tools":[{"type":"file_search","filters":null,"max_num_results":20,"ranking_options":{"ranker":"auto","score_threshold":0},"vector_store_ids":["vs_68caad8bd5d88191ab766cf043d89a18"]}],"top_logprobs":0,"top_p":1,"truncation":"disabled","usage":{"input_tokens":3748,"input_tokens_details":{"cached_tokens":2304},"output_tokens":543,"output_tokens_details":{"reasoning_tokens":448},"total_tokens":4291},"user":null,"metadata":{}}}
\ No newline at end of file
diff --git a/packages/azure/src/__fixtures__/openai-file-search-tool.2.json b/packages/azure/src/__fixtures__/openai-file-search-tool.2.json
new file mode 100644
index 000000000000..3a28b1fc87be
--- /dev/null
+++ b/packages/azure/src/__fixtures__/openai-file-search-tool.2.json
@@ -0,0 +1,112 @@
+{
+  "id": "resp_0365d26c32c64c650068cabb02fea4819495862c2bc58440ad",
+  "object": "response",
+  "created_at": 1758116611,
+  "status": "completed",
+  "background": false,
+  "error": null,
+  "incomplete_details": null,
+  "instructions": null,
+  "max_output_tokens": null,
+  "max_tool_calls": null,
+  "model": "gpt-5-mini-2025-08-07",
+  "output": [
+    {
+      "id": "rs_0365d26c32c64c650068cabb03bcc48194bfbd973152bca8f6",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "fs_0365d26c32c64c650068cabb04aa388194b53c59de50a3951e",
+      "type": "file_search_call",
+      "status": "completed",
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model in the document?",
+        "definition of embedding model",
+        "embedding model explanation 'embedding model'"
+      ],
+      "results": [
+        {
+          "attributes": {},
+          "file_id": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+          "filename": "ai.pdf",
+          "score": 0.9311,
+          "text": "AI 1\n\nAI\nGenerative artificial intelligence refers to models that predict and generate \nvarious types of outputs (such as text, images, or audio) based on whatʼs \nstatistically likely, pulling from patterns theyʼve learned from their training data. \nFor example:\n\nGiven a photo, a generative model can generate a caption.\n\nGiven an audio file, a generative model can generate a transcription.\n\nGiven a text description, a generative model can generate an image.\n\nA large language model LLM is a subset of generative models focused \nprimarily on text. An LLM takes a sequence of words as input and aims to \npredict the most likely sequence to follow. It assigns probabilities to potential \nnext sequences and then selects one. The model continues to generate \nsequences until it meets a specified stopping criterion.\n\nLLMs learn by training on massive collections of written text, which means they \nwill be better suited to some use cases than others. For example, a model \ntrained on GitHub data would understand the probabilities of sequences in \nsource code particularly well.\n\nHowever, it's crucial to understand LLMs' limitations. When asked about less \nknown or absent information, like the birthday of a personal relative, LLMs \nmight \"hallucinate\" or make up information. It's essential to consider how well-\nrepresented the information you need is in the model.\n\nAn embedding model is used to convert complex data (like words or images) \ninto a dense vector (a list of numbers) representation, known as an embedding. \nUnlike generative models, embedding models do not generate new text or data. \nInstead, they provide representations of semantic and synactic relationships \nbetween entities that can be used as input for other models or other natural \nlanguage processing tasks.\n\nIn the next section, you will learn about the difference between models \nproviders and models, and which ones are available in the AI SDK."
+        }
+      ]
+    },
+    {
+      "id": "rs_0365d26c32c64c650068cabb061740819491324d349d0f07ca",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "msg_0365d26c32c64c650068cabb0e66b081949f66f61dacef39f3",
+      "type": "message",
+      "status": "completed",
+      "content": [
+        {
+          "type": "output_text",
+          "annotations": [
+            {
+              "type": "file_citation",
+              "file_id": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+              "filename": "ai.pdf",
+              "index": 350
+            }
+          ],
+          "logprobs": [],
+          "text": "According to the document, an embedding model converts complex data (like words or images) into a dense vector — a list of numbers — called an embedding. It does not generate new text or data; instead it encodes semantic and syntactic relationships between entities so those vector representations can be used as inputs for other models or NLP tasks ."
+        }
+      ],
+      "role": "assistant"
+    }
+  ],
+  "parallel_tool_calls": true,
+  "previous_response_id": null,
+  "prompt_cache_key": null,
+  "reasoning": {
+    "effort": "medium",
+    "summary": null
+  },
+  "safety_identifier": null,
+  "service_tier": "default",
+  "store": true,
+  "temperature": 1,
+  "text": {
+    "format": {
+      "type": "text"
+    },
+    "verbosity": "medium"
+  },
+  "tool_choice": "auto",
+  "tools": [
+    {
+      "type": "file_search",
+      "filters": null,
+      "max_num_results": 20,
+      "ranking_options": {
+        "ranker": "auto",
+        "score_threshold": 0
+      },
+      "vector_store_ids": ["vs_68caad8bd5d88191ab766cf043d89a18"]
+    }
+  ],
+  "top_logprobs": 0,
+  "top_p": 1,
+  "truncation": "disabled",
+  "usage": {
+    "input_tokens": 3678,
+    "input_tokens_details": {
+      "cached_tokens": 2304
+    },
+    "output_tokens": 536,
+    "output_tokens_details": {
+      "reasoning_tokens": 448
+    },
+    "total_tokens": 4214
+  },
+  "user": null,
+  "metadata": {}
+}
diff --git a/packages/azure/src/__fixtures__/openai-image-generation-tool.1.json b/packages/azure/src/__fixtures__/openai-image-generation-tool.1.json
new file mode 100644
index 000000000000..52e9ff9f7a90
--- /dev/null
+++ b/packages/azure/src/__fixtures__/openai-image-generation-tool.1.json
@@ -0,0 +1,96 @@
+{
+  "id": "resp_0a33d15155cb126d0068c96c54970481958484dea31f07926d",
+  "object": "response",
+  "created_at": 1758030932,
+  "status": "completed",
+  "background": false,
+  "error": null,
+  "incomplete_details": null,
+  "instructions": null,
+  "max_output_tokens": null,
+  "max_tool_calls": null,
+  "model": "gpt-5-nano-2025-08-07",
+  "output": [
+    {
+      "id": "rs_0a33d15155cb126d0068c96c5527808195a933b468ccb5dfd9",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "ig_0a33d15155cb126d0068c96c59bc14819599154c9988b82996",
+      "type": "image_generation_call",
+      "status": "completed",
+      "background": "opaque",
+      "output_format": "webp",
+      "quality": "low",
+      "result": "UklGRoitEQBXRUJQVlA4TEGzEAAv/8P/AM1AbNtGkITZhU4fH9J/wTOT+xIi+j8BuT4kABkCibNAZrlwIPyQ7W/bH5L2RHMzMBKgXmfZeYi9tLtrrQkZvN1yXHLLgG71gPkpDxmI/gc03YFulQR...AA9lhA0Y9rVdqQs/W4w/MOxeRW5+R1/UXmmNVi9yQ7x/vG0q2VRk01seZIuj5OmRYhD+yY82dZqMH1BCueTeOcNfGKxQ==",
+      "revised_prompt": "A cute fluffy cat sitting on a sunlit windowsill, warm sunlight, soft fur, expressive eyes, photorealistic style.",
+      "size": "1024x1024"
+    },
+    {
+      "id": "rs_0a33d15155cb126d0068c96c6c0ef48195bc73e30faf832ba3",
+      "type": "reasoning",
+      "summary": []
+    },
+    {
+      "id": "msg_0a33d15155cb126d0068c96c723ed88195b1405bc370bb8a65",
+      "type": "message",
+      "status": "completed",
+      "content": [
+        {
+          "type": "output_text",
+          "annotations": [],
+          "logprobs": [],
+          "text": ""
+        }
+      ],
+      "role": "assistant"
+    }
+  ],
+  "parallel_tool_calls": true,
+  "previous_response_id": null,
+  "prompt_cache_key": null,
+  "reasoning": {
+    "effort": "medium",
+    "summary": null
+  },
+  "safety_identifier": null,
+  "service_tier": "default",
+  "store": true,
+  "temperature": 1,
+  "text": {
+    "format": {
+      "type": "text"
+    },
+    "verbosity": "medium"
+  },
+  "tool_choice": "auto",
+  "tools": [
+    {
+      "type": "image_generation",
+      "background": "auto",
+      "moderation": "auto",
+      "n": 1,
+      "output_compression": 100,
+      "output_format": "webp",
+      "quality": "low",
+      "size": "1024x1024"
+    }
+  ],
+  "top_logprobs": 0,
+  "top_p": 1,
+  "truncation": "disabled",
+  "usage": {
+    "input_tokens": 3151,
+    "input_tokens_details": {
+      "cached_tokens": 0
+    },
+    "output_tokens": 1970,
+    "output_tokens_details": {
+      "reasoning_tokens": 1920
+    },
+    "total_tokens": 5121
+  },
+  "user": null,
+  "metadata": {}
+}
diff --git a/packages/azure/src/__snapshots__/azure-openai-provider.test.ts.snap b/packages/azure/src/__snapshots__/azure-openai-provider.test.ts.snap
new file mode 100644
index 000000000000..ce1bbb95dd01
--- /dev/null
+++ b/packages/azure/src/__snapshots__/azure-openai-provider.test.ts.snap
@@ -0,0 +1,3098 @@
+// Vitest Snapshot v1, https://vitest.dev/guide/snapshot.html
+
+exports[`responses > doGenerate > code interpreter tool > should include code interpreter tool call and result in content 1`] = `
+[
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_02ffbc38ae6fca1a0068ec8762f61881979bf5191564930c23",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "input": "{"code":"import random\\n\\n# Generate five random numbers between 1 and 100 with two decimal places\\nnumbers = [round(random.uniform(1, 100), 2) for _ in range(5)]\\n\\n# Print the program and its execution results\\nprint(\\"Program: Generate five random numbers between 1 and 100 with two decimal places\\")\\nprint(\\"Generated numbers:\\")\\nfor i, num in enumerate(numbers, start=1):\\n    print(f\\"{i}: {num:.2f}\\")","containerId":"cntr_68ec87613e58819097ec47888dc686d8088a1d37157e7565"}",
+    "providerExecuted": true,
+    "toolCallId": "ci_02ffbc38ae6fca1a0068ec876654a08197af4d648fe8bba210",
+    "toolName": "code_interpreter",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "outputs": [
+        {
+          "logs": "Program: Generate five random numbers between 1 and 100 with two decimal places
+Generated numbers:
+1: 74.07
+2: 94.14
+3: 53.74
+4: 77.29
+5: 87.68
+",
+          "type": "logs",
+        },
+      ],
+    },
+    "toolCallId": "ci_02ffbc38ae6fca1a0068ec876654a08197af4d648fe8bba210",
+    "toolName": "code_interpreter",
+    "type": "tool-result",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_02ffbc38ae6fca1a0068ec8768f35c81979428e19fa11468f1",
+      },
+    },
+    "text": "I ran the program and produced five random numbers between 1 and 100 with two decimal places. Results shown above:
+
+1: 74.07
+2: 94.14
+3: 53.74
+4: 77.29
+5: 87.68
+
+If you want the code to be saved to a file, run multiple sets, or use a fixed seed for reproducible results, tell me and I can adjust.",
+    "type": "text",
+  },
+]
+`;
+
+exports[`responses > doGenerate > file search tool > with results include > should include file search tool call and result in content 1`] = `
+[
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0365d26c32c64c650068cabb03bcc48194bfbd973152bca8f6",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "input": "{}",
+    "providerExecuted": true,
+    "toolCallId": "fs_0365d26c32c64c650068cabb04aa388194b53c59de50a3951e",
+    "toolName": "file_search",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model in the document?",
+        "definition of embedding model",
+        "embedding model explanation 'embedding model'",
+      ],
+      "results": [
+        {
+          "attributes": {},
+          "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+          "filename": "ai.pdf",
+          "score": 0.9311,
+          "text": "AI 1
+
+AI
+Generative artificial intelligence refers to models that predict and generate 
+various types of outputs (such as text, images, or audio) based on whatʼs 
+statistically likely, pulling from patterns theyʼve learned from their training data. 
+For example:
+
+Given a photo, a generative model can generate a caption.
+
+Given an audio file, a generative model can generate a transcription.
+
+Given a text description, a generative model can generate an image.
+
+A large language model LLM is a subset of generative models focused 
+primarily on text. An LLM takes a sequence of words as input and aims to 
+predict the most likely sequence to follow. It assigns probabilities to potential 
+next sequences and then selects one. The model continues to generate 
+sequences until it meets a specified stopping criterion.
+
+LLMs learn by training on massive collections of written text, which means they 
+will be better suited to some use cases than others. For example, a model 
+trained on GitHub data would understand the probabilities of sequences in 
+source code particularly well.
+
+However, it's crucial to understand LLMs' limitations. When asked about less 
+known or absent information, like the birthday of a personal relative, LLMs 
+might "hallucinate" or make up information. It's essential to consider how well-
+represented the information you need is in the model.
+
+An embedding model is used to convert complex data (like words or images) 
+into a dense vector (a list of numbers) representation, known as an embedding. 
+Unlike generative models, embedding models do not generate new text or data. 
+Instead, they provide representations of semantic and synactic relationships 
+between entities that can be used as input for other models or other natural 
+language processing tasks.
+
+In the next section, you will learn about the difference between models 
+providers and models, and which ones are available in the AI SDK.",
+        },
+      ],
+    },
+    "toolCallId": "fs_0365d26c32c64c650068cabb04aa388194b53c59de50a3951e",
+    "toolName": "file_search",
+    "type": "tool-result",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0365d26c32c64c650068cabb061740819491324d349d0f07ca",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_0365d26c32c64c650068cabb0e66b081949f66f61dacef39f3",
+      },
+    },
+    "text": "According to the document, an embedding model converts complex data (like words or images) into a dense vector — a list of numbers — called an embedding. It does not generate new text or data; instead it encodes semantic and syntactic relationships between entities so those vector representations can be used as inputs for other models or NLP tasks .",
+    "type": "text",
+  },
+  {
+    "filename": "ai.pdf",
+    "id": "id-0",
+    "mediaType": "text/plain",
+    "providerMetadata": {
+      "openai": {
+        "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+      },
+    },
+    "sourceType": "document",
+    "title": "ai.pdf",
+    "type": "source",
+  },
+]
+`;
+
+exports[`responses > doGenerate > file search tool > without results include > should include file search tool call and result in content 1`] = `
+[
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0a098396a8feca410068caae3b47208196957fe59419daad70",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "input": "{}",
+    "providerExecuted": true,
+    "toolCallId": "fs_0a098396a8feca410068caae3cab5c8196a54fd00498464e62",
+    "toolName": "file_search",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model?",
+        "definition of embedding model in the document",
+        "embedding model description",
+      ],
+      "results": null,
+    },
+    "toolCallId": "fs_0a098396a8feca410068caae3cab5c8196a54fd00498464e62",
+    "toolName": "file_search",
+    "type": "tool-result",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0a098396a8feca410068caae3e21a081968e7ac588401c4a6a",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_0a098396a8feca410068caae457c508196b2fcd079d1d3ec74",
+      },
+    },
+    "text": "According to the document, an embedding model is used to convert complex data (like words or images) into a dense vector (a list of numbers) representation called an embedding, which captures semantic and syntactic relationships. Unlike generative models, embedding models do not generate new text or data; instead, they provide these vector representations to be used as input for other models or other natural language processing tasks .",
+    "type": "text",
+  },
+  {
+    "filename": "ai.pdf",
+    "id": "id-0",
+    "mediaType": "text/plain",
+    "providerMetadata": {
+      "openai": {
+        "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+      },
+    },
+    "sourceType": "document",
+    "title": "ai.pdf",
+    "type": "source",
+  },
+]
+`;
+
+exports[`responses > doStream > should send code interpreter calls 1`] = `
+[
+  {
+    "type": "stream-start",
+    "warnings": [],
+  },
+  {
+    "id": "resp_0ad69c3c5fcb01f60068eba78cacd08193bf9a57724aa55ab8",
+    "modelId": "gpt-5-mini",
+    "timestamp": 2025-10-12T13:05:16.000Z,
+    "type": "response-metadata",
+  },
+  {
+    "id": "rs_0ad69c3c5fcb01f60068eba78f12588193872253fcce2ef954:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0ad69c3c5fcb01f60068eba78f12588193872253fcce2ef954",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-start",
+  },
+  {
+    "id": "rs_0ad69c3c5fcb01f60068eba78f12588193872253fcce2ef954:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0ad69c3c5fcb01f60068eba78f12588193872253fcce2ef954",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-end",
+  },
+  {
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "providerExecuted": true,
+    "toolName": "code_interpreter",
+    "type": "tool-input-start",
+  },
+  {
+    "delta": "{"containerId":"cntr_68eba78d6dc081908116b7ad7a32f52d0050b146e60e47a9","code":"",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "#",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " Program",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ":",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " generate",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " five",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " random",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " numbers",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " between",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " ",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "1",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " and",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " ",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "100",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " with",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " two",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " decimal",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " places",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "\\n",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "import",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " random",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "\\n\\n",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "#",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " Generate",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " five",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " random",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " numbers",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " and",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " round",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " to",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " two",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " decimals",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "\\n",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "numbers",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " =",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " [",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "round",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "(random",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ".uniform",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "(",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "1",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ",",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " ",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "100",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "),",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " ",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "2",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ")",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " for",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " _",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " in",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " range",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
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+  },
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+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "5",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ")]\\n\\n",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "#",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " Print",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
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+  },
+  {
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+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " a",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
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+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " formatted",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " string",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
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+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "print",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
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+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "Generated",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
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+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " (",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "as",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
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+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " numbers",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": ")\\n",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
+    "delta": "print",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
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+    "type": "tool-input-delta",
+  },
+  {
+    "delta": " \\".",
+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
+  },
+  {
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+    "id": "ci_0ad69c3c5fcb01f60068eba7939b2c8193b42d4d517ca5d25f",
+    "type": "tool-input-delta",
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+    "type": "tool-input-delta",
+  },
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+Generated numbers (formatted to 2 decimal places): 88.99, 51.21, 89.85, 88.97, 7.73
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+    "type": "text-delta",
+  },
+  {
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+    "type": "text-delta",
+  },
+  {
+    "delta": " seed",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " save",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " the",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " program",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " to",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " a",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " file",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " for",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " you",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": ".",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " Want",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": " that",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "delta": "?",
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-delta",
+  },
+  {
+    "id": "msg_0ad69c3c5fcb01f60068eba7956c948193be6ab573376051d6",
+    "type": "text-end",
+  },
+  {
+    "error": [AI_JSONParseError: JSON parsing failed: Text: .
+Error message: Unexpected end of JSON input],
+    "type": "error",
+  },
+  {
+    "finishReason": "error",
+    "providerMetadata": {
+      "openai": {
+        "responseId": "resp_0ad69c3c5fcb01f60068eba78cacd08193bf9a57724aa55ab8",
+        "serviceTier": "default",
+      },
+    },
+    "type": "finish",
+    "usage": {
+      "cachedInputTokens": 1280,
+      "inputTokens": 2143,
+      "outputTokens": 919,
+      "reasoningTokens": 576,
+      "totalTokens": 3062,
+    },
+  },
+]
+`;
+
+exports[`responses > file search tool > should stream file search results with results include 1`] = `
+[
+  {
+    "type": "stream-start",
+    "warnings": [],
+  },
+  {
+    "id": "resp_06456cb9918b63780068cacd710b0881a1b00b5fca56e7100b",
+    "modelId": "gpt-5-mini-2025-08-07",
+    "timestamp": 2025-09-17T15:02:09.000Z,
+    "type": "response-metadata",
+  },
+  {
+    "id": "rs_06456cb9918b63780068cacd717c3481a1b8c7094da5770b41:0",
+    "providerMetadata": {
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+  },
+  {
+    "id": "rs_06456cb9918b63780068cacd717c3481a1b8c7094da5770b41:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_06456cb9918b63780068cacd717c3481a1b8c7094da5770b41",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-end",
+  },
+  {
+    "input": "{}",
+    "providerExecuted": true,
+    "toolCallId": "fs_06456cb9918b63780068cacd74a1dc81a1bf68dd57f140b4b6",
+    "toolName": "file_search",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model definition in this document?",
+        "How does the document define an embedding model?",
+      ],
+      "results": [
+        {
+          "attributes": {},
+          "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+          "filename": "ai.pdf",
+          "score": 0.9312,
+          "text": "AI 1
+
+AI
+Generative artificial intelligence refers to models that predict and generate 
+various types of outputs (such as text, images, or audio) based on whatʼs 
+statistically likely, pulling from patterns theyʼve learned from their training data. 
+For example:
+
+Given a photo, a generative model can generate a caption.
+
+Given an audio file, a generative model can generate a transcription.
+
+Given a text description, a generative model can generate an image.
+
+A large language model LLM is a subset of generative models focused 
+primarily on text. An LLM takes a sequence of words as input and aims to 
+predict the most likely sequence to follow. It assigns probabilities to potential 
+next sequences and then selects one. The model continues to generate 
+sequences until it meets a specified stopping criterion.
+
+LLMs learn by training on massive collections of written text, which means they 
+will be better suited to some use cases than others. For example, a model 
+trained on GitHub data would understand the probabilities of sequences in 
+source code particularly well.
+
+However, it's crucial to understand LLMs' limitations. When asked about less 
+known or absent information, like the birthday of a personal relative, LLMs 
+might "hallucinate" or make up information. It's essential to consider how well-
+represented the information you need is in the model.
+
+An embedding model is used to convert complex data (like words or images) 
+into a dense vector (a list of numbers) representation, known as an embedding. 
+Unlike generative models, embedding models do not generate new text or data. 
+Instead, they provide representations of semantic and synactic relationships 
+between entities that can be used as input for other models or other natural 
+language processing tasks.
+
+In the next section, you will learn about the difference between models 
+providers and models, and which ones are available in the AI SDK.",
+        },
+      ],
+    },
+    "toolCallId": "fs_06456cb9918b63780068cacd74a1dc81a1bf68dd57f140b4b6",
+    "toolName": "file_search",
+    "type": "tool-result",
+  },
+  {
+    "id": "rs_06456cb9918b63780068cacd7663c481a191dd84333d842728:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_06456cb9918b63780068cacd7663c481a191dd84333d842728",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-start",
+  },
+  {
+    "id": "rs_06456cb9918b63780068cacd7663c481a191dd84333d842728:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_06456cb9918b63780068cacd7663c481a191dd84333d842728",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-end",
+  },
+  {
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+      },
+    },
+    "type": "text-start",
+  },
+  {
+    "delta": "The",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " document",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " defines",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " an",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " embedding",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " model",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
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+    "type": "text-delta",
+  },
+  {
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
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+  },
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
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+    "type": "text-delta",
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+    "type": "text-delta",
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
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+    "type": "text-delta",
+  },
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+    "type": "text-delta",
+  },
+  {
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+    "type": "text-delta",
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+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
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+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
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+    "type": "text-delta",
+  },
+  {
+    "delta": " or",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " NLP",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " tasks",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "delta": " ",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "filename": "ai.pdf",
+    "id": "id-0",
+    "mediaType": "text/plain",
+    "providerMetadata": {
+      "openai": {
+        "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+      },
+    },
+    "sourceType": "document",
+    "title": "ai.pdf",
+    "type": "source",
+  },
+  {
+    "delta": ".",
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-delta",
+  },
+  {
+    "id": "msg_06456cb9918b63780068cacd7c922081a1ae15f2672a51980f",
+    "type": "text-end",
+  },
+  {
+    "finishReason": "stop",
+    "providerMetadata": {
+      "openai": {
+        "responseId": "resp_06456cb9918b63780068cacd710b0881a1b00b5fca56e7100b",
+        "serviceTier": "default",
+      },
+    },
+    "type": "finish",
+    "usage": {
+      "cachedInputTokens": 2304,
+      "inputTokens": 3748,
+      "outputTokens": 543,
+      "reasoningTokens": 448,
+      "totalTokens": 4291,
+    },
+  },
+]
+`;
+
+exports[`responses > file search tool > should stream file search results without results include 1`] = `
+[
+  {
+    "type": "stream-start",
+    "warnings": [],
+  },
+  {
+    "id": "resp_0459517ad68504ad0068cabfba22b88192836339640e9a765a",
+    "modelId": "gpt-5-mini-2025-08-07",
+    "timestamp": 2025-09-17T14:03:38.000Z,
+    "type": "response-metadata",
+  },
+  {
+    "id": "rs_0459517ad68504ad0068cabfba951881929654a05214361b35:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0459517ad68504ad0068cabfba951881929654a05214361b35",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-start",
+  },
+  {
+    "id": "rs_0459517ad68504ad0068cabfba951881929654a05214361b35:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0459517ad68504ad0068cabfba951881929654a05214361b35",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-end",
+  },
+  {
+    "input": "{}",
+    "providerExecuted": true,
+    "toolCallId": "fs_0459517ad68504ad0068cabfbd76888192a5dc4475fadabf8a",
+    "toolName": "file_search",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "queries": [
+        "What is an embedding model according to this document?",
+        "What is an embedding model defined as in the document?",
+        "definition of embedding model",
+      ],
+      "results": null,
+    },
+    "toolCallId": "fs_0459517ad68504ad0068cabfbd76888192a5dc4475fadabf8a",
+    "toolName": "file_search",
+    "type": "tool-result",
+  },
+  {
+    "id": "rs_0459517ad68504ad0068cabfbf337881929cf5266be7a008a9:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0459517ad68504ad0068cabfbf337881929cf5266be7a008a9",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-start",
+  },
+  {
+    "id": "rs_0459517ad68504ad0068cabfbf337881929cf5266be7a008a9:0",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0459517ad68504ad0068cabfbf337881929cf5266be7a008a9",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "type": "reasoning-end",
+  },
+  {
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+      },
+    },
+    "type": "text-start",
+  },
+  {
+    "delta": "According",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " to",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " the",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " document",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ",",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " an",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " embedding",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " model",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " converts",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " complex",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " data",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " (",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": "e",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ".g",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ".,",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " words",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " or",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " images",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ")",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " into",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " a",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " dense",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " vector",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " —",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " a",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " list",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " of",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " numbers",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " —",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " called",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " an",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " embedding",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " ",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "filename": "ai.pdf",
+    "id": "id-0",
+    "mediaType": "text/plain",
+    "providerMetadata": {
+      "openai": {
+        "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+      },
+    },
+    "sourceType": "document",
+    "title": "ai.pdf",
+    "type": "source",
+  },
+  {
+    "delta": ".",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " Unlike",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " gener",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": "ative",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " models",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ",",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " embedding",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " models",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " do",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " not",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " generate",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " new",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " text",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " or",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " data",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": ";",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " instead",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " they",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " produce",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " vector",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " representations",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " that",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " capture",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " semantic",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " and",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " synt",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": "actic",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " relationships",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " and",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " can",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " be",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " used",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " as",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " input",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " for",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " other",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " models",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " or",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " NLP",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " tasks",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "delta": " ",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "filename": "ai.pdf",
+    "id": "id-1",
+    "mediaType": "text/plain",
+    "providerMetadata": {
+      "openai": {
+        "fileId": "file-Ebzhf8H4DPGPr9pUhr7n7v",
+      },
+    },
+    "sourceType": "document",
+    "title": "ai.pdf",
+    "type": "source",
+  },
+  {
+    "delta": ".",
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-delta",
+  },
+  {
+    "id": "msg_0459517ad68504ad0068cabfc6b5c48192a15ac773668537f1",
+    "type": "text-end",
+  },
+  {
+    "finishReason": "stop",
+    "providerMetadata": {
+      "openai": {
+        "responseId": "resp_0459517ad68504ad0068cabfba22b88192836339640e9a765a",
+        "serviceTier": "default",
+      },
+    },
+    "type": "finish",
+    "usage": {
+      "cachedInputTokens": 2304,
+      "inputTokens": 3737,
+      "outputTokens": 621,
+      "reasoningTokens": 512,
+      "totalTokens": 4358,
+    },
+  },
+]
+`;
+
+exports[`responses > image generation tool > should include generate image tool call and result in content 1`] = `
+[
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0a33d15155cb126d0068c96c5527808195a933b468ccb5dfd9",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "input": "{}",
+    "providerExecuted": true,
+    "toolCallId": "ig_0a33d15155cb126d0068c96c59bc14819599154c9988b82996",
+    "toolName": "image_generation",
+    "type": "tool-call",
+  },
+  {
+    "providerExecuted": true,
+    "result": {
+      "result": "UklGRoitEQBXRUJQVlA4TEGzEAAv/8P/AM1AbNtGkITZhU4fH9J/wTOT+xIi+j8BuT4kABkCibNAZrlwIPyQ7W/bH5L2RHMzMBKgXmfZeYi9tLtrrQkZvN1yXHLLgG71gPkpDxmI/gc03YFulQR...AA9lhA0Y9rVdqQs/W4w/MOxeRW5+R1/UXmmNVi9yQ7x/vG0q2VRk01seZIuj5OmRYhD+yY82dZqMH1BCueTeOcNfGKxQ==",
+    },
+    "toolCallId": "ig_0a33d15155cb126d0068c96c59bc14819599154c9988b82996",
+    "toolName": "image_generation",
+    "type": "tool-result",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "rs_0a33d15155cb126d0068c96c6c0ef48195bc73e30faf832ba3",
+        "reasoningEncryptedContent": null,
+      },
+    },
+    "text": "",
+    "type": "reasoning",
+  },
+  {
+    "providerMetadata": {
+      "openai": {
+        "itemId": "msg_0a33d15155cb126d0068c96c723ed88195b1405bc370bb8a65",
+      },
+    },
+    "text": "",
+    "type": "text",
+  },
+]
+`;
diff --git a/packages/azure/src/azure-openai-provider.test.ts b/packages/azure/src/azure-openai-provider.test.ts
index 74e3752f937f..18c5e10e6bf6 100644
--- a/packages/azure/src/azure-openai-provider.test.ts
+++ b/packages/azure/src/azure-openai-provider.test.ts
@@ -1,10 +1,18 @@
 import {
   EmbeddingModelV3Embedding,
+  LanguageModelV3,
+  LanguageModelV3FunctionTool,
   LanguageModelV3Prompt,
 } from '@ai-sdk/provider';
+import {
+  convertReadableStreamToArray,
+  mockId,
+} from '@ai-sdk/provider-utils/test';
 import { createTestServer } from '@ai-sdk/test-server/with-vitest';
+import fs from 'node:fs';
+import { beforeEach, describe, it, expect, vi } from 'vitest';
+import { OpenAIResponsesLanguageModel } from '@ai-sdk/openai/internal';
 import { createAzure } from './azure-openai-provider';
-import { describe, it, expect, vi } from 'vitest';
 
 vi.mock('./version', () => ({
   VERSION: '0.0.0-test',
@@ -14,6 +22,68 @@ const TEST_PROMPT: LanguageModelV3Prompt = [
   { role: 'user', content: [{ type: 'text', text: 'Hello' }] },
 ];
 
+const TEST_TOOLS: Array<LanguageModelV3FunctionTool> = [
+  {
+    type: 'function',
+    name: 'weather',
+    inputSchema: {
+      type: 'object',
+      properties: { location: { type: 'string' } },
+      required: ['location'],
+      additionalProperties: false,
+    },
+  },
+  {
+    type: 'function',
+    name: 'cityAttractions',
+    inputSchema: {
+      type: 'object',
+      properties: { city: { type: 'string' } },
+      required: ['city'],
+      additionalProperties: false,
+    },
+  },
+];
+
+function prepareJsonFixtureResponse(filename: string) {
+  server.urls[
+    'https://test-resource.openai.azure.com/openai/v1/responses'
+  ].response = {
+    type: 'json-value',
+    body: JSON.parse(
+      fs.readFileSync(`src/__fixtures__/${filename}.json`, 'utf8'),
+    ),
+  };
+  return;
+}
+
+function prepareChunksFixtureResponse(filename: string) {
+  const chunks = fs
+    .readFileSync(`src/__fixtures__/${filename}.chunks.txt`, 'utf8')
+    .split('\n')
+    .map(line => `data: ${line}\n\n`);
+  chunks.push('data: [DONE]\n\n');
+
+  server.urls[
+    'https://test-resource.openai.azure.com/openai/v1/responses'
+  ].response = {
+    type: 'stream-chunks',
+    chunks,
+  };
+}
+
+function createModel(modelId: string) {
+  return new OpenAIResponsesLanguageModel(modelId, {
+    provider: 'azure.responses',
+    url: ({ path }) =>
+      `https://test-resource.openai.azure.com/openai/v1${path}`,
+    //headers: () => ({ Authorization: `Bearer APIKEY` }),
+    headers: () => ({ Authorization: `Bearer APIKEY` }),
+    generateId: mockId(),
+    fileIdPrefixes: ['assistant-'],
+  });
+}
+
 const provider = createAzure({
   resourceName: 'test-resource',
   apiKey: 'test-api-key',
@@ -48,7 +118,7 @@ describe('chat', () => {
           id: 'chatcmpl-95ZTZkhr0mHNKqerQfiwkuox3PHAd',
           object: 'chat.completion',
           created: 1711115037,
-          model: 'gpt-3.5-turbo-0125',
+          model: 'test-deployment',
           choices: [
             {
               index: 0,
@@ -167,7 +237,7 @@ describe('completion', () => {
           id: 'cmpl-96cAM1v77r4jXa4qb2NSmRREV5oWB',
           object: 'text_completion',
           created: 1711363706,
-          model: 'gpt-35-turbo-instruct',
+          model: 'test-deployment',
           choices: [
             {
               text: content,
@@ -183,7 +253,7 @@ describe('completion', () => {
     it('should set the correct api version', async () => {
       prepareJsonCompletionResponse({ content: 'Hello World!' });
 
-      await provider.completion('gpt-35-turbo-instruct').doGenerate({
+      await provider.completion('test-deployment').doGenerate({
         prompt: TEST_PROMPT,
       });
       expect(
@@ -202,7 +272,7 @@ describe('completion', () => {
         },
       });
 
-      await provider.completion('gpt-35-turbo-instruct').doGenerate({
+      await provider.completion('test-deployment').doGenerate({
         prompt: TEST_PROMPT,
         headers: {
           'Custom-Request-Header': 'request-header-value',
@@ -820,5 +890,630 @@ describe('responses', () => {
 
       expect(warnings).toStrictEqual([]);
     });
+
+    describe('code interpreter tool', () => {
+      let result: Awaited<ReturnType<LanguageModelV3['doGenerate']>>;
+
+      beforeEach(async () => {
+        prepareJsonFixtureResponse('azure-code-interpreter-tool.1');
+
+        result = await createModel('test-deployment').doGenerate({
+          prompt: TEST_PROMPT,
+          tools: [
+            {
+              type: 'provider-defined',
+              id: 'openai.code_interpreter',
+              name: 'code_interpreter',
+              args: {},
+            },
+          ],
+        });
+      });
+
+      it('should send request body with include and tool', async () => {
+        expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
+          {
+            "include": [
+              "code_interpreter_call.outputs",
+            ],
+            "input": [
+              {
+                "content": [
+                  {
+                    "text": "Hello",
+                    "type": "input_text",
+                  },
+                ],
+                "role": "user",
+              },
+            ],
+            "model": "test-deployment",
+            "tools": [
+              {
+                "container": {
+                  "type": "auto",
+                },
+                "type": "code_interpreter",
+              },
+            ],
+          }
+        `);
+      });
+
+      it('should include code interpreter tool call and result in content', async () => {
+        expect(result.content).toMatchSnapshot();
+      });
+    });
+
+    describe('file search tool', () => {
+      let result: Awaited<ReturnType<LanguageModelV3['doGenerate']>>;
+
+      describe('without results include', () => {
+        beforeEach(async () => {
+          prepareJsonFixtureResponse('openai-file-search-tool.1');
+
+          result = await createModel('test-deployment').doGenerate({
+            prompt: TEST_PROMPT,
+            tools: [
+              {
+                type: 'provider-defined',
+                id: 'openai.file_search',
+                name: 'file_search',
+                args: {
+                  vectorStoreIds: ['vs_68caad8bd5d88191ab766cf043d89a18'],
+                  maxNumResults: 5,
+                  filters: {
+                    key: 'author',
+                    type: 'eq',
+                    value: 'Jane Smith',
+                  },
+                  ranking: {
+                    ranker: 'auto',
+                    scoreThreshold: 0.5,
+                  },
+                },
+              },
+            ],
+          });
+        });
+
+        it('should send request body with tool', async () => {
+          expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
+          {
+            "input": [
+              {
+                "content": [
+                  {
+                    "text": "Hello",
+                    "type": "input_text",
+                  },
+                ],
+                "role": "user",
+              },
+            ],
+            "model": "test-deployment",
+            "tools": [
+              {
+                "filters": {
+                  "key": "author",
+                  "type": "eq",
+                  "value": "Jane Smith",
+                },
+                "max_num_results": 5,
+                "ranking_options": {
+                  "ranker": "auto",
+                  "score_threshold": 0.5,
+                },
+                "type": "file_search",
+                "vector_store_ids": [
+                  "vs_68caad8bd5d88191ab766cf043d89a18",
+                ],
+              },
+            ],
+          }
+        `);
+        });
+
+        it('should include file search tool call and result in content', async () => {
+          expect(result.content).toMatchSnapshot();
+        });
+      });
+
+      describe('with results include', () => {
+        beforeEach(async () => {
+          prepareJsonFixtureResponse('openai-file-search-tool.2');
+
+          result = await createModel('test-deployment').doGenerate({
+            prompt: TEST_PROMPT,
+            tools: [
+              {
+                type: 'provider-defined',
+                id: 'openai.file_search',
+                name: 'file_search',
+                args: {
+                  vectorStoreIds: ['vs_68caad8bd5d88191ab766cf043d89a18'],
+                  maxNumResults: 5,
+                  filters: {
+                    key: 'author',
+                    type: 'eq',
+                    value: 'Jane Smith',
+                  },
+                  ranking: {
+                    ranker: 'auto',
+                    scoreThreshold: 0.5,
+                  },
+                },
+              },
+            ],
+            providerOptions: {
+              openai: {
+                include: ['file_search_call.results'],
+              },
+            },
+          });
+        });
+
+        it('should send request body with tool', async () => {
+          expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
+            {
+              "include": [
+                "file_search_call.results",
+              ],
+              "input": [
+                {
+                  "content": [
+                    {
+                      "text": "Hello",
+                      "type": "input_text",
+                    },
+                  ],
+                  "role": "user",
+                },
+              ],
+              "model": "test-deployment",
+              "tools": [
+                {
+                  "filters": {
+                    "key": "author",
+                    "type": "eq",
+                    "value": "Jane Smith",
+                  },
+                  "max_num_results": 5,
+                  "ranking_options": {
+                    "ranker": "auto",
+                    "score_threshold": 0.5,
+                  },
+                  "type": "file_search",
+                  "vector_store_ids": [
+                    "vs_68caad8bd5d88191ab766cf043d89a18",
+                  ],
+                },
+              ],
+            }
+          `);
+        });
+
+        it('should include file search tool call and result in content', async () => {
+          expect(result.content).toMatchSnapshot();
+        });
+      });
+    });
+  });
+
+  describe('image generation tool', () => {
+    let result: Awaited<ReturnType<LanguageModelV3['doGenerate']>>;
+
+    beforeEach(async () => {
+      prepareJsonFixtureResponse('openai-image-generation-tool.1');
+
+      result = await createModel('test-deployment').doGenerate({
+        prompt: TEST_PROMPT,
+        tools: [
+          {
+            type: 'provider-defined',
+            id: 'openai.image_generation',
+            name: 'image_generation',
+            args: {
+              outputFormat: 'webp',
+              quality: 'low',
+              size: '1024x1024',
+              partialImages: 2,
+            },
+          },
+        ],
+      });
+    });
+
+    it('should send request body with include and tool', async () => {
+      expect(await server.calls[0].requestBodyJson).toMatchInlineSnapshot(`
+          {
+            "input": [
+              {
+                "content": [
+                  {
+                    "text": "Hello",
+                    "type": "input_text",
+                  },
+                ],
+                "role": "user",
+              },
+            ],
+            "model": "test-deployment",
+            "tools": [
+              {
+                "output_format": "webp",
+                "partial_images": 2,
+                "quality": "low",
+                "size": "1024x1024",
+                "type": "image_generation",
+              },
+            ],
+          }
+        `);
+    });
+
+    it('should include generate image tool call and result in content', async () => {
+      expect(result.content).toMatchSnapshot();
+    });
+  });
+
+  describe('doStream', () => {
+    it('should stream text deltas', async () => {
+      server.urls[
+        'https://test-resource.openai.azure.com/openai/v1/responses'
+      ].response = {
+        type: 'stream-chunks',
+        chunks: [
+          `data:{"type":"response.created","response":{"id":"resp_67c9a81b6a048190a9ee441c5755a4e8","object":"response","created_at":1741269019,"status":"in_progress","error":null,"incomplete_details":null,"input":[],"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0.3,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}\n\n`,
+          `data:{"type":"response.in_progress","response":{"id":"resp_67c9a81b6a048190a9ee441c5755a4e8","object":"response","created_at":1741269019,"status":"in_progress","error":null,"incomplete_details":null,"input":[],"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0.3,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}\n\n`,
+          `data:{"type":"response.output_item.added","output_index":0,"item":{"id":"msg_67c9a81dea8c8190b79651a2b3adf91e","type":"message","status":"in_progress","role":"assistant","content":[]}}\n\n`,
+          `data:{"type":"response.content_part.added","item_id":"msg_67c9a81dea8c8190b79651a2b3adf91e","output_index":0,"content_index":0,"part":{"type":"output_text","text":"","annotations":[],"logprobs": []}}\n\n`,
+          `data:{"type":"response.output_text.delta","item_id":"msg_67c9a81dea8c8190b79651a2b3adf91e","output_index":0,"content_index":0,"delta":"Hello,","logprobs": []}\n\n`,
+          `data:{"type":"response.output_text.delta","item_id":"msg_67c9a81dea8c8190b79651a2b3adf91e","output_index":0,"content_index":0,"delta":" World!","logprobs": []}\n\n`,
+          `data:{"type":"response.output_text.done","item_id":"msg_67c9a8787f4c8190b49c858d4c1cf20c","output_index":0,"content_index":0,"text":"Hello, World!"}\n\n`,
+          `data:{"type":"response.content_part.done","item_id":"msg_67c9a8787f4c8190b49c858d4c1cf20c","output_index":0,"content_index":0,"part":{"type":"output_text","text":"Hello, World!","annotations":[],"logprobs": []}}\n\n`,
+          `data:{"type":"response.output_item.done","output_index":0,"item":{"id":"msg_67c9a8787f4c8190b49c858d4c1cf20c","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Hello, World!","annotations":[],"logprobs": []}]}}\n\n`,
+          `data:{"type":"response.completed","response":{"id":"resp_67c9a878139c8190aa2e3105411b408b","object":"response","created_at":1741269112,"status":"completed","error":null,"incomplete_details":null,"input":[],"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[{"id":"msg_67c9a8787f4c8190b49c858d4c1cf20c","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Hello, World!","annotations":[]}]}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0.3,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1,"truncation":"disabled","usage":{"input_tokens":543,"input_tokens_details":{"cached_tokens":234},"output_tokens":478,"output_tokens_details":{"reasoning_tokens":123},"total_tokens":512},"user":null,"metadata":{}}}\n\n`,
+        ],
+      };
+
+      const { stream } = await createModel('test-deployment').doStream({
+        prompt: TEST_PROMPT,
+        includeRawChunks: false,
+      });
+
+      expect(await convertReadableStreamToArray(stream)).toMatchInlineSnapshot(`
+        [
+          {
+            "type": "stream-start",
+            "warnings": [],
+          },
+          {
+            "id": "resp_67c9a81b6a048190a9ee441c5755a4e8",
+            "modelId": "test-deployment",
+            "timestamp": 2025-03-06T13:50:19.000Z,
+            "type": "response-metadata",
+          },
+          {
+            "id": "msg_67c9a81dea8c8190b79651a2b3adf91e",
+            "providerMetadata": {
+              "openai": {
+                "itemId": "msg_67c9a81dea8c8190b79651a2b3adf91e",
+              },
+            },
+            "type": "text-start",
+          },
+          {
+            "delta": "Hello,",
+            "id": "msg_67c9a81dea8c8190b79651a2b3adf91e",
+            "type": "text-delta",
+          },
+          {
+            "delta": " World!",
+            "id": "msg_67c9a81dea8c8190b79651a2b3adf91e",
+            "type": "text-delta",
+          },
+          {
+            "id": "msg_67c9a8787f4c8190b49c858d4c1cf20c",
+            "type": "text-end",
+          },
+          {
+            "finishReason": "stop",
+            "providerMetadata": {
+              "openai": {
+                "responseId": "resp_67c9a81b6a048190a9ee441c5755a4e8",
+              },
+            },
+            "type": "finish",
+            "usage": {
+              "cachedInputTokens": 234,
+              "inputTokens": 543,
+              "outputTokens": 478,
+              "reasoningTokens": 123,
+              "totalTokens": 1021,
+            },
+          },
+        ]
+      `);
+    });
+
+    it('should send streaming tool calls', async () => {
+      server.urls[
+        'https://test-resource.openai.azure.com/openai/v1/responses'
+      ].response = {
+        type: 'stream-chunks',
+        chunks: [
+          `data:{"type":"response.created","response":{"id":"resp_67cb13a755c08190acbe3839a49632fc","object":"response","created_at":1741362087,"status":"in_progress","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[{"type":"function","description":"Get the current location.","name":"currentLocation","parameters":{"type":"object","properties":{},"additionalProperties":false},"strict":true},{"type":"function","description":"Get the weather in a location","name":"weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The location to get the weather for"}},"required":["location"],"additionalProperties":false},"strict":true}],"top_p":1,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}\n\n`,
+          `data:{"type":"response.in_progress","response":{"id":"resp_67cb13a755c08190acbe3839a49632fc","object":"response","created_at":1741362087,"status":"in_progress","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[{"type":"function","description":"Get the current location.","name":"currentLocation","parameters":{"type":"object","properties":{},"additionalProperties":false},"strict":true},{"type":"function","description":"Get the weather in a location","name":"weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The location to get the weather for"}},"required":["location"],"additionalProperties":false},"strict":true}],"top_p":1,"truncation":"disabled","usage":null,"user":null,"metadata":{}}}\n\n`,
+          `data:{"type":"response.output_item.added","output_index":0,"item":{"type":"function_call","id":"fc_67cb13a838088190be08eb3927c87501","call_id":"call_6KxSghkb4MVnunFH2TxPErLP","name":"currentLocation","arguments":"","status":"completed"}}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a838088190be08eb3927c87501","output_index":0,"delta":"{}"}\n\n`,
+          `data:{"type":"response.function_call_arguments.done","item_id":"fc_67cb13a838088190be08eb3927c87501","output_index":0,"arguments":"{}"}\n\n`,
+          `data:{"type":"response.output_item.done","output_index":0,"item":{"type":"function_call","id":"fc_67cb13a838088190be08eb3927c87501","call_id":"call_pgjcAI4ZegMkP6bsAV7sfrJA","name":"currentLocation","arguments":"{}","status":"completed"}}\n\n`,
+          `data:{"type":"response.output_item.added","output_index":1,"item":{"type":"function_call","id":"fc_67cb13a858f081908a600343fa040f47","call_id":"call_Dg6WUmFHNeR5JxX1s53s1G4b","name":"weather","arguments":"","status":"in_progress"}}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"delta":"{"}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"delta":"\\"location"}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"delta":"\\":"}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"delta":"\\"Rome"}\n\n`,
+          `data:{"type":"response.function_call_arguments.delta","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"delta":"\\"}"}\n\n`,
+          `data:{"type":"response.function_call_arguments.done","item_id":"fc_67cb13a858f081908a600343fa040f47","output_index":1,"arguments":"{\\"location\\":\\"Rome\\"}"}\n\n`,
+          `data:{"type":"response.output_item.done","output_index":1,"item":{"type":"function_call","id":"fc_67cb13a858f081908a600343fa040f47","call_id":"call_X2PAkDJInno9VVnNkDrfhboW","name":"weather","arguments":"{\\"location\\":\\"Rome\\"}","status":"completed"}}\n\n`,
+          `data:{"type":"response.completed","response":{"id":"resp_67cb13a755c08190acbe3839a49632fc","object":"response","created_at":1741362087,"status":"completed","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[{"type":"function_call","id":"fc_67cb13a838088190be08eb3927c87501","call_id":"call_KsVqaVAf3alAtCCkQe4itE7W","name":"currentLocation","arguments":"{}","status":"completed"},{"type":"function_call","id":"fc_67cb13a858f081908a600343fa040f47","call_id":"call_X2PAkDJInno9VVnNkDrfhboW","name":"weather","arguments":"{\\"location\\":\\"Rome\\"}","status":"completed"}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[{"type":"function","description":"Get the current location.","name":"currentLocation","parameters":{"type":"object","properties":{},"additionalProperties":false},"strict":true},{"type":"function","description":"Get the weather in a location","name":"weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The location to get the weather for"}},"required":["location"],"additionalProperties":false},"strict":true}],"top_p":1,"truncation":"disabled","usage":{"input_tokens":0,"input_tokens_details":{"cached_tokens":0},"output_tokens":0,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":0},"user":null,"metadata":{}}}\n\n`,
+        ],
+      };
+
+      const { stream } = await createModel('test-deployment').doStream({
+        tools: TEST_TOOLS,
+        prompt: TEST_PROMPT,
+        includeRawChunks: false,
+      });
+
+      expect(await convertReadableStreamToArray(stream)).toMatchInlineSnapshot(`
+        [
+          {
+            "type": "stream-start",
+            "warnings": [],
+          },
+          {
+            "id": "resp_67cb13a755c08190acbe3839a49632fc",
+            "modelId": "test-deployment",
+            "timestamp": 2025-03-07T15:41:27.000Z,
+            "type": "response-metadata",
+          },
+          {
+            "id": "call_6KxSghkb4MVnunFH2TxPErLP",
+            "toolName": "currentLocation",
+            "type": "tool-input-start",
+          },
+          {
+            "delta": "{}",
+            "id": "call_6KxSghkb4MVnunFH2TxPErLP",
+            "type": "tool-input-delta",
+          },
+          {
+            "id": "call_pgjcAI4ZegMkP6bsAV7sfrJA",
+            "type": "tool-input-end",
+          },
+          {
+            "input": "{}",
+            "providerMetadata": {
+              "openai": {
+                "itemId": "fc_67cb13a838088190be08eb3927c87501",
+              },
+            },
+            "toolCallId": "call_pgjcAI4ZegMkP6bsAV7sfrJA",
+            "toolName": "currentLocation",
+            "type": "tool-call",
+          },
+          {
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "toolName": "weather",
+            "type": "tool-input-start",
+          },
+          {
+            "delta": "{",
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "type": "tool-input-delta",
+          },
+          {
+            "delta": ""location",
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "type": "tool-input-delta",
+          },
+          {
+            "delta": "":",
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "type": "tool-input-delta",
+          },
+          {
+            "delta": ""Rome",
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "type": "tool-input-delta",
+          },
+          {
+            "delta": ""}",
+            "id": "call_Dg6WUmFHNeR5JxX1s53s1G4b",
+            "type": "tool-input-delta",
+          },
+          {
+            "id": "call_X2PAkDJInno9VVnNkDrfhboW",
+            "type": "tool-input-end",
+          },
+          {
+            "input": "{"location":"Rome"}",
+            "providerMetadata": {
+              "openai": {
+                "itemId": "fc_67cb13a858f081908a600343fa040f47",
+              },
+            },
+            "toolCallId": "call_X2PAkDJInno9VVnNkDrfhboW",
+            "toolName": "weather",
+            "type": "tool-call",
+          },
+          {
+            "finishReason": "tool-calls",
+            "providerMetadata": {
+              "openai": {
+                "responseId": "resp_67cb13a755c08190acbe3839a49632fc",
+              },
+            },
+            "type": "finish",
+            "usage": {
+              "cachedInputTokens": 0,
+              "inputTokens": 0,
+              "outputTokens": 0,
+              "reasoningTokens": 0,
+              "totalTokens": 0,
+            },
+          },
+        ]
+      `);
+    });
+
+    it('should handle file_citation annotations without optional fields in streaming', async () => {
+      server.urls[
+        'https://test-resource.openai.azure.com/openai/v1/responses'
+      ].response = {
+        type: 'stream-chunks',
+        chunks: [
+          `data:{"type":"response.content_part.added","item_id":"msg_456","output_index":0,"content_index":0,"part":{"type":"output_text","text":"","annotations":[]}}\n\n`,
+          `data:{"type":"response.output_text.annotation.added","item_id":"msg_456","output_index":0,"content_index":0,"annotation_index":0,"annotation":{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":145}}\n\n`,
+          `data:{"type":"response.output_text.annotation.added","item_id":"msg_456","output_index":0,"content_index":0,"annotation_index":1,"annotation":{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":192}}\n\n`,
+          `data:{"type":"response.content_part.done","item_id":"msg_456","output_index":0,"content_index":0,"part":{"type":"output_text","text":"Answer for the specified years....","annotations":[{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":145},{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":192}]}}\n\n`,
+          `data:{"type":"response.output_item.done","output_index":0,"item":{"id":"msg_456","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Answer for the specified years....","annotations":[{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":145},{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":192}]}]}}\n\n`,
+          `data:{"type":"response.completed","response":{"id":"resp_456","object":"response","created_at":1234567890,"status":"completed","error":null,"incomplete_details":null,"instructions":null,"max_output_tokens":null,"model":"test-deployment","output":[{"id":"msg_456","type":"message","status":"completed","role":"assistant","content":[{"type":"output_text","text":"Answer for the specified years....","annotations":[{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":145},{"type":"file_citation","file_id":"assistant-YRcoCqn3Fo2K4JgraG","filename":"resource1.json","index":192}]}]}],"parallel_tool_calls":true,"previous_response_id":null,"reasoning":{"effort":null,"summary":null},"store":true,"temperature":0,"text":{"format":{"type":"text"}},"tool_choice":"auto","tools":[],"top_p":1,"truncation":"disabled","usage":{"input_tokens":50,"input_tokens_details":{"cached_tokens":0},"output_tokens":25,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":75},"user":null,"metadata":{}}}\n\n`,
+          'data: [DONE]\n\n',
+        ],
+      };
+
+      const { stream } = await createModel('test-deployment').doStream({
+        prompt: TEST_PROMPT,
+        includeRawChunks: false,
+      });
+
+      expect(await convertReadableStreamToArray(stream)).toMatchInlineSnapshot(`
+        [
+          {
+            "type": "stream-start",
+            "warnings": [],
+          },
+          {
+            "filename": "resource1.json",
+            "id": "id-0",
+            "mediaType": "text/plain",
+            "providerMetadata": {
+              "openai": {
+                "fileId": "assistant-YRcoCqn3Fo2K4JgraG",
+              },
+            },
+            "sourceType": "document",
+            "title": "resource1.json",
+            "type": "source",
+          },
+          {
+            "filename": "resource1.json",
+            "id": "id-1",
+            "mediaType": "text/plain",
+            "providerMetadata": {
+              "openai": {
+                "fileId": "assistant-YRcoCqn3Fo2K4JgraG",
+              },
+            },
+            "sourceType": "document",
+            "title": "resource1.json",
+            "type": "source",
+          },
+          {
+            "id": "msg_456",
+            "type": "text-end",
+          },
+          {
+            "finishReason": "stop",
+            "providerMetadata": {
+              "openai": {
+                "responseId": null,
+              },
+            },
+            "type": "finish",
+            "usage": {
+              "cachedInputTokens": 0,
+              "inputTokens": 50,
+              "outputTokens": 25,
+              "reasoningTokens": 0,
+              "totalTokens": 75,
+            },
+          },
+        ]
+      `);
+    });
+
+    it('should send code interpreter calls', async () => {
+      prepareChunksFixtureResponse('azure-code-interpreter-tool.1');
+
+      const result = await createModel('test-deployment').doStream({
+        prompt: TEST_PROMPT,
+        tools: [
+          {
+            type: 'provider-defined',
+            id: 'openai.code_interpreter',
+            name: 'code_interpreter',
+            args: {},
+          },
+        ],
+      });
+
+      expect(
+        await convertReadableStreamToArray(result.stream),
+      ).toMatchSnapshot();
+    });
+  });
+  describe('file search tool', () => {
+    it('should stream file search results without results include', async () => {
+      prepareChunksFixtureResponse('openai-file-search-tool.1');
+
+      const result = await createModel('test-deployment').doStream({
+        prompt: TEST_PROMPT,
+        tools: [
+          {
+            type: 'provider-defined',
+            id: 'openai.file_search',
+            name: 'file_search',
+            args: {
+              vectorStoreIds: ['vs_68caad8bd5d88191ab766cf043d89a18'],
+            },
+          },
+        ],
+      });
+
+      expect(
+        await convertReadableStreamToArray(result.stream),
+      ).toMatchSnapshot();
+    });
+
+    it('should stream file search results with results include', async () => {
+      prepareChunksFixtureResponse('openai-file-search-tool.2');
+
+      const result = await createModel('test-deployment').doStream({
+        prompt: TEST_PROMPT,
+        tools: [
+          {
+            type: 'provider-defined',
+            id: 'openai.file_search',
+            name: 'file_search',
+            args: {
+              vectorStoreIds: ['vs_68caad8bd5d88191ab766cf043d89a18'],
+            },
+          },
+        ],
+        providerOptions: {
+          openai: {
+            include: ['file_search_call.results'],
+          },
+        },
+      });
+
+      expect(
+        await convertReadableStreamToArray(result.stream),
+      ).toMatchSnapshot();
+    });
   });
 });