Ative o Code Interpreter Python integrado ao chamar um modelo. O modelo escreve e executa código Python em um sandbox para resolver problemas complexos, como cálculos matemáticos e análise de dados.
Como usar
O Code Interpreter oferece suporte a três métodos de invocação. Os parâmetros variam conforme o método:
OpenAI-compatible - Responses API
Para ativar o Code Interpreter, adicione a ferramenta code_interpreter ao parâmetro tools.
Para obter os melhores resultados, ative simultaneamente as ferramentas
code_interpreter,web_searcheweb_extractor.
# 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)
OpenAI-compatible - Chat Completions API
Para ativar o Code Interpreter, passe enable_code_interpreter: true na solicitação da API.
# Import dependencies and create the client...
completion = client.chat.completions.create(
# Use a model that supports Code Interpreter
model="qwen3-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
)
O protocolo compatível com OpenAI não retorna detalhes da execução do código.
DashScope
Para ativar o Code Interpreter, defina enable_code_interpreter como true na solicitação da API.
# 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
)
O código executado é retornado no campo tool_info.
Após a ativação do Code Interpreter, o modelo processa as solicitações nas seguintes etapas:
- Raciocínio: O modelo analisa a solicitação do usuário e gera ideias e etapas para resolver o problema.
- Execução de código: O modelo gera e executa código Python.
- Integração de resultados: O modelo recebe o resultado da execução do código e planeja as próximas etapas.
- Resposta: O modelo gera uma resposta em linguagem natural.
As etapas 2 e 3 podem se repetir várias vezes.
Os campos retornados variam conforme a API:
- Responses API: O conteúdo de raciocínio é retornado em um objeto com
type="reasoning"na saída. A execução do código vem comtype="code_interpreter_call". A resposta final vem comtype="message". - Chat Completions API / DashScope: O conteúdo de raciocínio aparece no campo
reasoning_content. A resposta final vem no campocontent. O DashScope também permite retornar o conteúdo do código no campotool_info.
Escopo
Modelos recomendados
Responses API
Qwen-Max: Séries Qwen3,8-Max, Qwen3,7-Max
Qwen-Plus: Séries Qwen3,7-Plus, Qwen3,6-Plus, Qwen3,5-Plus
DeepSeek: deepseek-v4-flash, deepseek-v4-flash-0731, deepseek-v4-pro
GLM: glm-5.2
Série open source Qwen3,8
Chat Completions API / DashScope
- Qwen-Max (modo de raciocínio): Série Qwen3-Max
- Qwen-Plus: Série Qwen3,5-Plus
Outros modelos
Estes modelos também oferecem suporte ao Code Interpreter, mas podem apresentar desempenho inferior. O suporte está disponível apenas pela Responses API.
- Qwen-Flash: Séries Qwen3,7-Flash, Qwen3,6-Flash, Qwen3,5-Flash
- Série open-source Qwen3,6 (exceto qwen3.6-27b)
- Série open source Qwen3,5
Primeiros passos
Os exemplos a seguir demonstram como o Code Interpreter resolve problemas matemáticos.
OpenAI-compatible - Responses API
Para obter os melhores resultados, ative simultaneamente as ferramentas
code_interpreter,web_searcheweb_extractor.
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)
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();
# 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
}'
Exemplo de resposta
====================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"),
# 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
Exemplo de resposta
====================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
// 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();
Exemplo de resposta
====================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
# 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
}'
Exemplo de resposta
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]
DashScope
Não há suporte para o SDK Java.
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)
Exemplo de resposta
{"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": "qwen3.5-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"
}
}'
Exemplo de resposta
O texto
<...text content...>é um comentário explicativo e não faz parte da resposta real da API. Este comentário identifica as diferentes etapas de processamento.
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"}
Análise de respostas
OpenAI-compatible - Responses API
O exemplo abaixo, usando o SDK Python da OpenAI, demonstra como analisar uma resposta em streaming.
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_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}")
# Se o modelo não invocar realmente o interpretador de código (por exemplo, perguntas simples respondidas diretamente), a resposta não terá o campo x_tools
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")
DashScope
O exemplo a seguir, usando o SDK Python do DashScope, realiza dois cálculos em uma única solicitação e analisa o código retornado e a contagem de chamadas.
A API Chat Completions da OpenAI não retorna dados durante a etapa de execução de código , portanto, nenhuma resposta é enviada entre as etapas de raciocínio e integração de resultados . Ambas as etapas retornam conteúdo pelo campo
reasoning_content, permitindo processá-las conjuntamente como a etapa de raciocínio . Para ver um exemplo de análise de resposta, consulte o código na seção Getting started .
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)
Exemplo de resposta
====================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}}
Observações
-
O Code Interpreter e o Function calling são mutuamente exclusivos.
Ativar ambos na mesma solicitação causa um erro.
-
Com o Code Interpreter ativado, uma única solicitação pode acionar múltiplas inferências do modelo. O campo
usageresume o consumo total de tokens de todas as chamadas nessa solicitação.
Faturamento
O Code Interpreter é gratuito por tempo limitado, mas aumenta o consumo de tokens.