# Code Interpreter > Enable the built-in Python Code Interpreter when calling a model. The model writes and runs Python code in a sandbox to solve complex problems such as mathematical calculations and data analytics. ## How to use Code Interpreter supports three invocation methods. The parameters differ for each method: To enable Code Interpreter, add the `code_interpreter` tool to the `tools` parameter. > For the best results, enable the `code_interpreter`, `web_search`, and `web_extractor` tools at the same time. ```python # Import dependencies and create the client... response = client.responses.create( model="qwen3.8-max", input="What is 123 to the power of 21?", tools=[ {"type": "code_interpreter"}, {"type": "web_search"}, {"type": "web_extractor"}, ], extra_body={ "enable_thinking": True } ) print(response.output_text) ``` To enable Code Interpreter, pass `enable_code_interpreter: true` in the API request. ```python # Import dependencies and create the client... completion = client.chat.completions.create( # Use a model that supports Code Interpreter model="qwen3.8-max", messages=[{"role": "user", "content": "What is 123 to the power of 21?"}], # Because enable_code_interpreter is not a standard OpenAI parameter, you must pass it through extra_body when using the Python SDK. When using the Node.js SDK, pass it as a top-level parameter. extra_body={ "enable_code_interpreter": True, # The Code Interpreter feature only supports calls in thinking mode "enable_thinking": True, }, # Only streaming output calls are supported stream=True ) ``` > The OpenAI-compatible protocol does not return code execution details. To enable Code Interpreter, set `enable_code_interpreter` to `true` in the API request. ```python # Import dependencies... response = dashscope.MultiModalConversation.call( # If the environment variable is not configured, replace the next line with: api_key="sk-xxx", using your Model Studio API key. api_key=os.getenv("DASHSCOPE_API_KEY"), model="qwen3.5-plus", messages=[{"role": "user", "content": "What is 123 to the power of 21?"}], # Enable Code Interpreter using the enable_code_interpreter parameter enable_code_interpreter=True, # The Code Interpreter feature only supports thinking mode enable_thinking=True, result_format="message", # Only streaming output calls are supported stream=True ) ``` The executed code is returned in the `tool_info` field. After Code Interpreter is enabled, the model processes requests in these stages: 1. Thinking: The model analyzes the user's request and generates ideas and steps to solve the problem. 2. Code execution: The model generates and executes Python code. 3. Result integration: The model receives the code execution result and plans the next steps. 4. Response: The model generates a natural language response. > Steps 2 and 3 may loop multiple times. The fields returned by different APIs vary: - Responses API: Thinking content is returned in an object with `type="reasoning"` in the output. Code execution is returned with `type="code_interpreter_call"`. The response is returned with `type="message"`. - Chat Completions API / DashScope: Thinking content is returned in the `reasoning_content` field. The response is returned in the `content` field. DashScope also supports returning code content in the `tool_info` field. ## Scope `deepseek-v4.1-flash-1017` supports the built-in `code_interpreter` tool in the Responses API in all six regions and corresponding deployment scopes listed on the [model page](/help/en/model-studio/deepseek-v4-1-flash). ### Recommended models Qwen-Max: Qwen3.8-Max series, Qwen3.7-Max series Qwen-Plus: Qwen3.7-Plus series, Qwen3.6-Plus series, Qwen3.5-Plus series DeepSeek: deepseek-v4-flash-0731, deepseek-v4-pro GLM: glm-5.2 Qwen3.8 open source series - Qwen-Max (thinking mode): Qwen3-Max series - Qwen-Plus: Qwen3.5-Plus series ### Other models These models also support Code Interpreter but may not perform as well. Only supported through the Responses API. - Qwen-Flash: Qwen3.7-Flash series, Qwen3.6-Flash series, Qwen3.5-Flash series - Qwen3.6 open-source series (except qwen3.6-27b) - Qwen3.5 open source series ## Getting started These examples show how Code Interpreter solves mathematical problems. > For the best results, enable the `code_interpreter`, `web_search`, and `web_extractor` tools at the same time. ```python expandable import os from openai import OpenAI client = OpenAI( # If the environment variable is not configured, replace the next line with: api_key="sk-xxx", using your Model Studio API key. api_key=os.getenv("DASHSCOPE_API_KEY"), # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" ) response = client.responses.create( model="qwen3.8-max", input="12 to the power of 3", tools=[ { "type": "code_interpreter" }, { "type": "web_search" }, { "type": "web_extractor" } ], extra_body = { "enable_thinking": True } ) # Uncomment the following line to view the intermediate process output # print(response.output) print("="*20+"Response Content"+"="*20) print(response.output_text) print("="*20+"Token Consumption and Tool Calls"+"="*20) print(response.usage) ``` ```javascript expandable import OpenAI from "openai"; import process from 'process'; const openai = new OpenAI({ // If the environment variable is not configured, replace the next line with: apiKey: "sk-xxx", using your Model Studio API key. apiKey: process.env.DASHSCOPE_API_KEY, // The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" }); async function main() { const response = await openai.responses.create({ model: "qwen3.8-max", input: "Calculate 12 to the power of 3", tools: [ { type: "code_interpreter" }, { type: "web_search" }, { type: "web_extractor" } ], enable_thinking: true }); console.log("====================Response Content===================="); console.log(response.output_text); // Print the number of tool calls console.log("====================Token Consumption and Tool Calls===================="); if (response.usage && response.usage.x_tools) { console.log(`Code Interpreter runs: ${response.usage.x_tools.code_interpreter?.count || 0}`); } // Uncomment the following line to view the intermediate process output // console.log(JSON.stringify(response.output[0], null, 2)); } main(); ``` ```bash # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/responses \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3.8-max", "input": "Calculate 12 to the power of 3", "tools": [ {"type": "code_interpreter"}, {"type": "web_search"}, {"type": "web_extractor"} ], "enable_thinking": true }' ``` ###### Example response ```plaintext ====================Response Content==================== 12 to the power of 3 is **1728**. Calculation process: 12³ = 12 × 12 × 12 = 144 × 12 = 1728 ====================Token Consumption and Tool Calls==================== ResponseUsage(input_tokens=1160, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=195, output_tokens_details=OutputTokensDetails(reasoning_tokens=105), total_tokens=1355, x_tools={'code_interpreter': {'count': 1}}) ``` ```python expandable from openai import OpenAI import os # Initialize the OpenAI client client = OpenAI( # If the environment variable is not configured, replace with your Alibaba Cloud Model Studio API key: api_key="sk-xxx" api_key=os.getenv("DASHSCOPE_API_KEY"), # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1", ) messages = [{"role": "user", "content": "What is 123 to the power of 21?"}] completion = client.chat.completions.create( model="qwen3.5-plus", messages=messages, extra_body={"enable_thinking": True, "enable_code_interpreter": True}, stream=True, stream_options={ "include_usage": True }, ) reasoning_content = "" # Complete thinking process answer_content = "" # Complete response is_answering = False # Flag to check if the response phase has started print("\n" + "=" * 20 + "Thinking Process" + "=" * 20 + "\n") for chunk in completion: if not chunk.choices: print("\nUsage:") print(chunk.usage) continue delta = chunk.choices[0].delta # Collect only the thinking content if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None: if not is_answering: print(delta.reasoning_content, end="", flush=True) reasoning_content += delta.reasoning_content # When content is received, start the response if hasattr(delta, "content") and delta.content: if not is_answering: print("\n" + "=" * 20 + "Complete Response" + "=" * 20 + "\n") is_answering = True print(delta.content, end="", flush=True) answer_content += delta.content ``` ##### Example response ```plaintext ====================Thinking Process==================== The user is asking for the value of 123 to the power of 21. This is a mathematical calculation problem. I need to calculate 123^21. I can use the code calculator to compute this value. I need to call the code_interpreter function and pass the Python code to calculate 123**21. Let me construct this function call. The user asked for 123 to the power of 21, and I calculated the result using Python code. The calculation shows that 123 to the power of 21 equals 77269364466549865653073473388030061522211723. This is a very large number, and I should provide it directly. ====================Complete Response==================== 123 to the power of 21 is: 77269364466549865653073473388030061522211723 Usage: CompletionUsage(completion_tokens=245, prompt_tokens=719, total_tokens=964, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=None, audio_tokens=None, reasoning_tokens=153, rejected_prediction_tokens=None), prompt_tokens_details=None) ``` ```javascript expandable import OpenAI from "openai"; import process from 'process'; // Initialize the OpenAI client const openai = new OpenAI({ apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variables // The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. baseURL: 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1' }); let reasoningContent = ''; let answerContent = ''; let isAnswering = false; async function main() { try { const messages = [{ role: 'user', content: 'What is 123 to the power of 21?' }]; const stream = await openai.chat.completions.create({ model: 'qwen3.5-plus', messages, stream: true, enable_thinking: true, enable_code_interpreter: true }); console.log('\n' + '='.repeat(20) + 'Thinking Process' + '='.repeat(20) + '\n'); for await (const chunk of stream) { if (!chunk.choices?.length) { console.log('\nUsage:'); console.log(chunk.usage); continue; } const delta = chunk.choices[0].delta; // Collect only the thinking content if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) { if (!isAnswering) { process.stdout.write(delta.reasoning_content); } reasoningContent += delta.reasoning_content; } // When content is received, start the response if (delta.content !== undefined && delta.content) { if (!isAnswering) { console.log('\n' + '='.repeat(20) + 'Complete Response' + '='.repeat(20) + '\n'); isAnswering = true; } process.stdout.write(delta.content); answerContent += delta.content; } } } catch (error) { console.error('Error:', error); } } main(); ``` ##### Example response ```plaintext ====================Thinking Process==================== The user is asking for the value of 123 raised to the power of 21. This is a mathematical calculation that I can perform using Python's code interpreter. I'll use the exponentiation operator ** to calculate this. Let me write the code to compute 123**21.The calculation has been completed successfully. The result of 123 raised to the power of 21 is a very large number: 77269364466549865653073473388030061522211723. I should present this result clearly to the user. ====================Complete Response==================== 123 to the power of 21 is: 77269364466549865653073473388030061522211723 ``` ```bash # The following URL is for the China (Beijing) region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3.5-plus", "messages": [ { "role": "user", "content": "What is 123 to the power of 21?" } ], "enable_code_interpreter": true, "enable_thinking": true, "stream": true }' ``` #### Example response ```json data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"finish_reason":null,"logprobs":null,"delta":{"content":null,"reasoning_content":"The user"},"index":0}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"delta":{"content":null,"reasoning_content":" is asking"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"delta":{"content":null,"reasoning_content":" for"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} ... data: {"choices":[{"delta":{"content":"is a very large number, with a total","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"delta":{"content":"of 43 digits","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"delta":{"content":".","reasoning_content":null},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1761899724,"system_fingerprint":null,"model":"qwen3.8-max","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"} data: [DONE] ``` > The Java SDK is not supported. ```python import os import dashscope # China (Beijing) region. Replace {WorkspaceId} with your actual Workspace ID. URLs vary by region. dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1' messages = [ {"role": "user", "content": "What is 123 to the power of 21?"}, ] response = dashscope.MultiModalConversation.call( # If the environment variable is not configured, replace the next line with: api_key="sk-xxx", using your Model Studio API key. api_key=os.getenv("DASHSCOPE_API_KEY"), model="qwen3.5-plus", messages=messages, enable_code_interpreter=True, enable_thinking=True, result_format="message", # Only streaming output is supported stream=True ) for chunk in response: output = chunk["output"] print(output) ``` ##### Example response ```json expandable {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": "The"}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " user is asking"}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " me"}}]} ... {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " I'll write a"}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " simple Python program to calculate"}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": "The"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " user"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " asked"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} ... {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " I should present this result"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " to the user in"}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "", "reasoning_content": " a clear format."}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "123 to the power of ", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "21 is:\n\n", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "772693", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "644665", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "498656", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "530734", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "733880", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "300615", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "222117", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "null", "message": {"role": "assistant", "content": "23", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} {"text": null, "finish_reason": null, "choices": [{"finish_reason": "stop", "message": {"role": "assistant", "content": "", "reasoning_content": ""}}], "tool_info": [{"code_interpreter": {"code": "123**21"}, "type": "code_interpreter"}]} ``` ```bash curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \ -H "Authorization: Bearer $DASHSCOPE_API_KEY" \ -H "Content-Type: application/json" \ -H "X-DashScope-SSE: enable" \ -d '{ "model": "qwen3.5-plus", "input":{ "messages":[ { "role": "user", "content": "What is 123 to the power of 21?" } ] }, "parameters": { "enable_code_interpreter": true, "enable_thinking": true, "result_format": "message" } }' ``` #### Example response > The text `<...text content...>` is an explanatory comment and not part of the actual API response. This comment is used to identify the different processing stages. ```json expandable id:1 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"The","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":290,"output_tokens":3,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":1}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} id:2 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" user is asking","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":293,"output_tokens":6,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":4}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ...Thinking stage... id:21 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":388,"output_tokens":101,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":68}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ...Thinking ends, starting Code Interpreter... id:22 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":388,"output_tokens":101,"input_tokens":287,"output_tokens_details":{"reasoning_tokens":68},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ...Thinking starts after running Code Interpreter... id:23 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"The","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":838,"output_tokens":104,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":69},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} id:24 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" user","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":839,"output_tokens":105,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":70},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ...Thinking stage... id:43 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":" a clear format.","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":942,"output_tokens":208,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ...Thinking ends, starting response... id:44 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"123 to the power of","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":947,"output_tokens":213,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ... id:53 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"23","reasoning_content":"","role":"assistant"},"finish_reason":"null"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":997,"output_tokens":263,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} id:54 event:result :HTTP_STATUS/200 data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}],"tool_info":[{"code_interpreter":{"code":"123**21"},"type":"code_interpreter"}]},"usage":{"total_tokens":997,"output_tokens":263,"input_tokens":734,"output_tokens_details":{"reasoning_tokens":171},"plugins":{"code_interpreter":{"count":1}}},"request_id":"a1959ad1-2637-4672-a21f-4d351371d254"} ``` ## Parsing responses The following OpenAI Python SDK example shows how to parse a streaming response. ```python expandable import os from openai import OpenAI client = OpenAI( api_key=os.getenv("DASHSCOPE_API_KEY"), # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID. URLs vary by region. base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1" ) response = client.responses.create( model="qwen3.8-max", input="12 to the power of 3", tools=[ {"type": "code_interpreter"} ], extra_body={ "enable_thinking": True }, stream=True ) def print_section(title): print(f"\n{'=' * 20}{title}{'=' * 20}") current_section = None final_response = None for event in response: # Incremental output of the thinking process if event.type == "response.reasoning_text.delta": if current_section != "reasoning": print_section("Thinking Process") current_section = "reasoning" print(event.delta, end="", flush=True) # Code Interpreter call completed elif event.type == "response.output_item.done" and hasattr(event.item, "code"): print_section("Code Execution") print(f"Code:\n{event.item.code}") if event.item.outputs: print(f"Result: {event.item.outputs[0].logs}") current_section = "code" # Incremental output of the final response elif event.type == "response.output_text.delta": if current_section != "answer": print_section("Complete Response") current_section = "answer" print(event.delta, end="", flush=True) # Response completed, save the final result to get usage elif event.type == "response.completed": final_response = event.response # Output token consumption and number of tool calls if final_response and final_response.usage: print_section("Token Consumption and Tool Calls") usage = final_response.usage print(f"Input Tokens: {usage.input_tokens}") print(f"Output Tokens: {usage.output_tokens}") print(f"Thinking Tokens: {usage.output_tokens_details.reasoning_tokens}") # If the model does not actually invoke the code interpreter (e.g., simple questions answered directly), the response has no x_tools field if hasattr(usage, 'x_tools'): print(f"Code Interpreter calls: {usage.x_tools.get('code_interpreter', {}).get('count', 0)}") else: print("Code Interpreter calls: 0") ``` The following DashScope Python SDK example performs two calculations in a single request and parses the returned code and call count. > The OpenAI Chat Completions API does not return data during the code execution stage, so no response is sent between the thinking and result integration stages. Both stages return content through the `reasoning_content` field, so you can process them together as the thinking stage. For a response parsing example, see the code in the [Getting started](/help/en/model-studio/qwen-code-interpreter) section. ```python expandable import os from dashscope import MultiModalConversation # China (Beijing) region. Replace {WorkspaceId} with your actual Workspace ID. URLs vary by region. dashscope.base_http_api_url = 'https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1' messages = [{"role": "user", "content": "Run Code Interpreter twice: first, calculate the value of 123 to the power of 23. Second, divide the result by 5."}] response = MultiModalConversation.call( api_key=os.getenv("DASHSCOPE_API_KEY"), model="qwen3.5-plus", messages=messages, result_format="message", enable_thinking=True, enable_code_interpreter=True, stream=True, incremental_output=True, ) # Status flags: track if tool info is printed, if the answering phase has started, and if in the reasoning section is_answering = False in_reasoning_section = False cur_tools = [] # Print a section with a title def print_section(title): print(f"\n{'=' * 20}{title}{'=' * 20}") # Initially print the "Thinking Process" title print_section("Thinking Process") in_reasoning_section = True # Process each block returned by the model in a stream for chunk in response: try: # Extract key fields from the response: content, reasoning text, tool call info choice = chunk.output.choices[0] msg = choice.message content = msg.get("content", "") # Final answer content reasoning = msg.get("reasoning_content", "") # Reasoning process text tools = chunk.output.get("tool_info", None) # Tool call information except (IndexError, AttributeError, KeyError): # Skip blocks with abnormal structure continue # If there is no valid content, skip the current block if not content and not reasoning and tools is None: continue # Output the reasoning process if reasoning and not is_answering: if not in_reasoning_section: print_section("Thinking Process") in_reasoning_section = True print(reasoning, end="", flush=True) if tools is not None and tools != cur_tools: print_section("Tool Information") print(tools) in_reasoning_section = False cur_tools = tools # Output the final answer content if content: if not is_answering: print_section("Complete Response") is_answering = True in_reasoning_section = False print(content, end="", flush=True) # Print the number of Code Interpreter calls print_section("Code Interpreter Runs") print(chunk.usage.plugins) ``` ### Example response ```plaintext expandable ====================Thinking Process==================== The user wants to run Code Interpreter twice: 1. First run: Calculate the value of 123 to the power of 23. 2. Second run: Divide the result of the first run by 5. I need to first call Code Interpreter to calculate 123**23, then use this result to call Code Interpreter again to divide by 5. Let me do the first calculation. ====================Tool Information==================== [{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}] ====================Thinking Process==================== The first calculation yielded the value of 123 to the power of 23: 1169008215014432917465348578887506800769541157267 Now for the second run, I need to divide this result by 5. I need to use this exact value for the division. ====================Tool Information==================== [{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}, {'code_interpreter': {'code': ''}, 'type': 'code_interpreter'}] ====================Tool Information==================== [{'code_interpreter': {'code': '123**23'}, 'type': 'code_interpreter'}, {'code_interpreter': {'code': '1169008215014432917465348578887506800769541157267 / 5'}, 'type': 'code_interpreter'}] ====================Thinking Process==================== The user requested to run Code Interpreter twice: 1. First, calculate 123 to the power of 23. The result is: 1169008215014432917465348578887506800769541157267 2. Second, divide this result by 5. The result is: 2.338016430028866e+47 Now I need to report these two results to the user. ====================Complete Response==================== First run result: 123 to the power of 23 = 1169008215014432917465348578887506800769541157267 Second run result: The above result divided by 5 = 2.338016430028866e+47 ====================Code Interpreter Runs==================== {'code_interpreter': {'count': 2}} ``` ## Notes - Code Interpreter and [Function calling](/help/en/model-studio/qwen-function-calling) are mutually exclusive. > Enabling both in the same request causes an error. - When Code Interpreter is enabled, a single request may trigger multiple model inferences. The `usage` field summarizes the total token consumption for all calls in that request. ## Billing Code Interpreter is free for a limited time but increases token consumption.