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Code Interpreter

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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:

OpenAI-compatible - Responses API

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.
# Import dependencies and create the client...
response = client.responses.create(
    model="qwen3.7-plus",
    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)

OpenAI-compatible - Chat Completions API

To enable Code Interpreter, pass enable_code_interpreter: true in the API request.

# Import dependencies and create the client...
completion = client.chat.completions.create(
    # Use a model that supports Code Interpreter
    model="qwen3.7-plus",
    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.

DashScope

To enable Code Interpreter, set enable_code_interpreter to true in the API request.

# 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="qwen3.7-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

Recommended models

Responses API

Qwen-Max: Qwen3.7-Max series

Qwen-Plus: Qwen3.7-Plus series, Qwen3.6-Plus series, Qwen3.5-Plus series

Chat Completions API / DashScope

  • Qwen-Max (thinking mode): Qwen3-Max series

  • Qwen-Plus: Qwen3.7-Plus series, Qwen3.6-Plus series, Qwen3.5-Plus series

Other models

These models also support Code Interpreter but may not perform as well.

  • Qwen-Flash: 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.

OpenAI-compatible - Responses API

For the best results, enable the code_interpreter, web_search, and web_extractor tools at the same time.
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 China (Beijing) region. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.7-plus",
    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)
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 China (Beijing) region. URLs vary by region.
    baseURL: "https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
});

async function main() {
    const response = await openai.responses.create({
        model: "qwen3.7-plus",
        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();
# The following URL is for the China (Beijing) region. URLs vary by region.
curl -X POST https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/responses \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen3.7-plus",
    "input": "Calculate 12 to the power of 3",
    "tools": [
        {"type": "code_interpreter"},
        {"type": "web_search"},
        {"type": "web_extractor"}
    ],
    "enable_thinking": true
}'
Example response
====================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}})

OpenAI-compatible - Chat Completions API

Python
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"),
    # To use a model in the Singapore region, replace with "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
    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="qwen3.7-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
====================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)
Node.js
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
    // To use a model in the Singapore region, replace with https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1
    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: 'qwen3.7-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
====================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
curl
# To use a model in the Singapore region, replace with https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions
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": "qwen3.7-plus",
    "messages": [
        {
            "role": "user", 
            "content": "What is 123 to the power of 21?"
        }
    ],
    "enable_code_interpreter": true,
    "enable_thinking": true,
    "stream": true
}'

Example response

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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","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":"qwen3.7-plus","id":"chatcmpl-2f96ef0b-5924-4dfc-b768-4d53ec538b4e"}

data: [DONE]

DashScope

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="qwen3.7-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
{"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"}]}
curl
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": "qwen3.7-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.
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

OpenAI-compatible - Responses API

The following OpenAI Python SDK example shows how to parse a streaming response.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the China (Beijing) region. URLs vary by region.
    base_url="https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1"
)

response = client.responses.create(
    model="qwen3.7-plus",
    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_summary_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}")
    print(f"Code Interpreter calls: {usage.x_tools.get('code_interpreter', {}).get('count', 0)}")

DashScope

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 section.
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="qwen3.7-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

====================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 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.

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