Alibaba Cloud Model Studio:Deep thinking

Sample code

from openai import OpenAI
import os

# Initialize the OpenAI client.
client = OpenAI(
    # API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If an environment variable is not configured, provide your Model Studio API key directly: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the base_url based on your region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]

completion = client.chat.completions.create(
    model="qwen-plus",  # You can replace this with other deep-thinking models as needed.
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Full thinking process
answer_content = ""  # Full response
is_answering = False  # Tracks 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 reasoning 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 responding.
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Response

====================Thinking process====================

The user's query "Who are you?" requires an accurate and friendly response. The answer should first establish my identity as Qwen, developed by Tongyi Lab at Alibaba Cloud. It will then outline key capabilities such as question answering, text generation, and logical reasoning. The language must be simple and the tone approachable. To encourage interaction, I will invite the user to ask more questions. Finally, I'll check that all key details are present, including my name (Qwen) and developer, to provide a comprehensive answer.
====================Full response====================

Hello! I am Qwen, a large language model developed by Tongyi Lab at Alibaba Group. I can answer questions, generate text, perform logical reasoning, write code, and more, to provide you with high-quality information and services. You can call me Qwen. How can I help you?

Sample code

import OpenAI from "openai";
import process from 'process';

// Initialize the OpenAI client.
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from an environment variable.
    // The following is the base URL for the Singapore region. If you use models in the US (Virginia) region, change the base URL to https://dashscope-us.aliyuncs.com/compatible-mode/v1. The base URL varies by region. Update it for your deployment's region.
    baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;

async function main() {
    try {
        const messages = [{ role: 'user', content: 'Who are you' }];
        const stream = await openai.chat.completions.create({
            model: 'qwen-plus',
            messages,
            stream: true,
            enable_thinking: 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 reasoning 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 responding.
            if (delta.content !== undefined && delta.content) {
                if (!isAnswering) {
                    console.log('\n' + '='.repeat(20) + 'Full response' + '='.repeat(20) + '\n');
                    isAnswering = true;
                }
                process.stdout.write(delta.content);
                answerContent += delta.content;
            }
        }
    } catch (error) {
        console.error('Error:', error);
    }
}

main();

Response

====================Thinking process====================

The user's direct query "Who are you?" requires a concise and clear response. The answer will state my identity as Qwen, a large language model from Alibaba Cloud. It will mention key functions like question answering, text generation, and logical reasoning, and highlight multilingual support (Chinese, English). To remain concise, use cases will be mentioned briefly, if at all. The tone will be friendly, and the response will end with an invitation for further questions. Finally, I'll check to ensure accuracy without including unnecessary details like version numbers.
====================Full response====================

I am Qwen, a large language model developed by Tongyi Lab at Alibaba Group. I can perform a variety of tasks, including answering questions, generating text, logical reasoning, and coding, and I support multiple languages, including Chinese and English. If you have any questions or need help, feel free to ask me at any time!

Sample code

# ======= Important =======
# The following URL is for the Singapore region. For the China (Beijing) region, replace the URL with: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions
# For the US (Virginia) region, replace it with: https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# === Remove this comment before execution ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "messages": [
        {
            "role": "user", 
            "content": "Who are you"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true
}'

Response

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

.....

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":10,"completion_tokens":360,"total_tokens":370},"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

Sample code

import os
from dashscope import Generation
import dashscope

# Configurations vary by region. Modify this value based on your region.
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"

# Initialize request parameters
messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
    # If an environment variable is not configured, replace the following line with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    stream=True,
    incremental_output=True,
)

# Full thinking process
reasoning_content = ""
# Full response
answer_content = ""
# Tracks if the response phase has started.
is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    # If both the thinking and response content are empty, ignore the chunk.
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # If the current chunk is part of the thinking process.
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        # If the current chunk is part of the response.
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Full response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

# To print the full thinking process and full response, uncomment the following code.
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Response

====================Thinking process====================
To answer the query "Who are you?", the response must state my identity as Qwen, a large language model from Alibaba Cloud. It will then explain my purpose as a helpful assistant by outlining key functions like question answering, text generation, and logical reasoning. The response will maintain a conversational tone, avoiding jargon. To encourage further engagement, it will end with an open-ended question. Finally, I'll check for clarity, conciseness, and a balance between a friendly and professional tone.
====================Full response====================
Hello! I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, generate text, perform logical reasoning, write code, and more, to provide help and support. Whether you have a question about daily life or a professional topic, I will do my best to help. Is there anything I can help you with?

Sample code

// The version of the DashScope SDK must be 2.19.4 or later.
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // The following base_url is for the Singapore region. If you use a model in the Virginia region, you must change the base_url to https://dashscope-us.aliyuncs.com/api/v1.
        // Configurations vary by region. Modify the configuration based on your actual region.
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
    }
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================Thinking Process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================Complete Response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                 // API keys vary by region. To obtain an API key, visit https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If you have not configured the environment variable, replace the following line with your Alibaba Cloud Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-plus")
                .enableThinking(true)
                .incrementalOutput(true)
                .resultFormat("message")
                .messages(Arrays.asList(userMsg))
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
//             Print the final result.
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Complete Response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

Response

====================Thinking process====================
The response to "Who are you?" must be based on my predefined identity as Qwen, a large language model from Alibaba Cloud. The answer will be conversational, concise, and easy to understand. It will first state my identity, then explain my functions, including text creation, logical reasoning, coding, and multilingual support. The tone will be friendly, and the response will end with an invitation for the user to ask for help, to encourage further interaction.
====================Full response====================
Hello! I am Qwen, a large language model from Alibaba Group. I can answer questions; create text such as stories, official documents, emails, and scripts; perform logical reasoning; write code; express opinions; and even play games. I am proficient in multiple languages, including but not limited to Chinese, English, German, French, and Spanish. Is there anything I can help you with?

Sample code

# ======= Important =======
# API keys vary by region. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
# The following is the URL for the Singapore region. For the China (Beijing) region, replace the URL with: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# For the US (Virginia) region, replace the URL with: https://dashscope-us.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === Remove this comment before execution ===
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwen-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Response

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"Hmm","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":14,"input_tokens":11,"output_tokens":3},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":",","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":15,"input_tokens":11,"output_tokens":4},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"user","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":16,"input_tokens":11,"output_tokens":5},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:4
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"asks","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":17,"input_tokens":11,"output_tokens":6},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:5
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"“","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":18,"input_tokens":11,"output_tokens":7},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}
......

id:358
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"help","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":373,"input_tokens":11,"output_tokens":362},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:359
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":",","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":374,"input_tokens":11,"output_tokens":363},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:360
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"Please feel free","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":375,"input_tokens":11,"output_tokens":364},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:361
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"to","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":376,"input_tokens":11,"output_tokens":365},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:362
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"let me know","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":377,"input_tokens":11,"output_tokens":366},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:363
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"!","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

id:364
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":378,"input_tokens":11,"output_tokens":367},"request_id":"25d58c29-c47b-9e8d-a0f1-d6c309ec58b1"}

Sample code

from openai import OpenAI
import os

# Initialize the OpenAI client.
client = OpenAI(
    # If the environment variable is not configured, replace "sk-xxx" with your Model Studio API key.
    # API keys are region-specific. To get an API key, visit https://www.alibabacloud.com/help/en/model-studio/get-api-key.
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify the base_url according to your region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

messages = [{"role": "user", "content": "Who are you"}]

completion = client.chat.completions.create(
    model="qwen-plus",
    messages=messages,
    # The enable_thinking parameter enables the thinking process, and thinking_budget sets its token limit.
    extra_body={
        "enable_thinking": True,
        "thinking_budget": 50
        },
    stream=True,
    stream_options={
        "include_usage": True
    },
)

reasoning_content = ""  # Complete thinking process
answer_content = ""  # Complete response
is_answering = False  # Tracks 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, the response phase begins.
    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

Response

====================Thinking process====================

Okay, the user is asking, "Who are you?" I need to provide a clear and friendly response. First, I should state my identity as Qwen, developed by Tongyi Lab at Alibaba Group. Next, I need to explain my main functions, such as answering
====================Complete response====================

I am Qwen, a large-scale language model developed by Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code.

Sample code

import OpenAI from "openai";
import process from 'process';

// Initialize the OpenAI client.
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from an environment variable.
    // Configurations vary by region. Modify the baseURL according to your region.
    baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
});

let reasoningContent = '';
let answerContent = '';
let isAnswering = false;

async function main() {
    try {
        const messages = [{ role: 'user', content: 'Who are you' }];
        const stream = await openai.chat.completions.create({
            model: 'qwen-plus',
            messages,
            stream: true,
            // The enable_thinking parameter enables the thinking process, and thinking_budget sets its token limit.
            enable_thinking: true,
            thinking_budget: 50
        });
        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, the response phase begins.
            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();

Response

====================Thinking process====================

Okay, the user is asking, "Who are you?" I need to provide a clear and accurate response. First, I should state my identity as Qwen, developed by Tongyi Lab at Alibaba Group. Next, I should explain my main functions, such as answering questions
====================Complete response====================

I am Qwen, a large-scale language model developed by Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code.

Sample code

# ======= Important =======
# The following is the base URL for the Singapore region. For models in the China (Beijing) region, replace the URL with: https://{WorkspaceId}.cn-beijing.maas.aliyuncs.com/compatible-mode/v1/chat/completions
# For models in the US (Virginia) region, replace the URL with: https://dashscope-us.aliyuncs.com/compatible-mode/v1/chat/completions
# === Remove this comment before execution ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
    "model": "qwen-plus",
    "messages": [
        {
            "role": "user", 
            "content": "Who are you"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true,
    "thinking_budget": 50
}'

Response

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

.....

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":10,"completion_tokens":360,"total_tokens":370},"created":1745485391,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-e2edaf2c-8aaf-9e54-90e2-b21dd5045503"}

data: [DONE]

Sample code

import os
from dashscope import Generation
import dashscope

# The following URL is for the Singapore region. When calling, replace WorkspaceId with your actual workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/"

messages = [{"role": "user", "content": "Who are you?"}]

completion = Generation.call(
    # If the environment variable is not configured, replace the following line with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    # Sets the token limit for the thinking process.
    thinking_budget=50,
    stream=True,
    incremental_output=True,
)

# Stores the complete thinking process.
reasoning_content = ""
# Stores the complete response.
answer_content = ""
# Tracks if the response phase has started.
is_answering = False

print("=" * 20 + "Thinking process" + "=" * 20)

for chunk in completion:
    # Ignore chunks where both thinking content and response content are empty.
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # If the current chunk contains thinking content.
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        # If the current chunk contains response content.
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Complete response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

# To print the complete thinking process and response, uncomment and run the following code.
# print("=" * 20 + "Complete thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Complete response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Response

====================Thinking process====================
Okay, the user is asking, "Who are you?" I need to provide a clear and friendly response. First, I must introduce myself as Qwen, developed by Tongyi Lab at Alibaba Group. Next, I should explain my main functions, such as
====================Complete response====================
I am Qwen, a large-scale language model developed by Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code.

Sample code

// The DashScope SDK version must be 2.19.4 or later.
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // The base HTTP API URL varies by region. Modify it according to your region.
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
    }
    private static final Logger logger = LoggerFactory.getLogger(Main.class);
    private static StringBuilder reasoningContent = new StringBuilder();
    private static StringBuilder finalContent = new StringBuilder();
    private static boolean isFirstPrint = true;

    private static void handleGenerationResult(GenerationResult message) {
        String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
        String content = message.getOutput().getChoices().get(0).getMessage().getContent();

        if (!reasoning.isEmpty()) {
            reasoningContent.append(reasoning);
            if (isFirstPrint) {
                System.out.println("====================Thinking process====================");
                isFirstPrint = false;
            }
            System.out.print(reasoning);
        }

        if (!content.isEmpty()) {
            finalContent.append(content);
            if (!isFirstPrint) {
                System.out.println("\n====================Complete response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // If the environment variable is not configured, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen-plus")
                .enableThinking(true)
                .thinkingBudget(50)
                .incrementalOutput(true)
                .resultFormat("message")
                .messages(Arrays.asList(userMsg))
                .build();
    }
    public static void streamCallWithMessage(Generation gen, Message userMsg)
            throws NoApiKeyException, ApiException, InputRequiredException {
        GenerationParam param = buildGenerationParam(userMsg);
        Flowable<GenerationResult> result = gen.streamCall(param);
        result.blockingForEach(message -> handleGenerationResult(message));
    }

    public static void main(String[] args) {
        try {
            Generation gen = new Generation();
            Message userMsg = Message.builder().role(Role.USER.getValue()).content("Who are you?").build();
            streamCallWithMessage(gen, userMsg);
//             Print the final result.
//            if (reasoningContent.length() > 0) {
//                System.out.println("\n====================Complete response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

====================Thinking process====================
Okay, the user is asking, "Who are you?" I need to provide a clear and friendly response. First, I must introduce myself as Qwen, developed by Tongyi Lab at Alibaba Group. Next, I should explain my main functions, such as
====================Complete response====================
I am Qwen, a large-scale language model developed by Tongyi Lab at Alibaba Group. I can answer questions, create text, perform logical reasoning, and write code.

Sample code

# ======= Important =======
# API keys are region-specific. To get an API key, visit https://www.alibabacloud.com/help/en/model-studio/get-api-key.
# The endpoint URL varies by region. Modify it according to your region.
# === Remove this comment before execution ===
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
    "model": "qwen-plus",
    "input":{
        "messages":[      
            {
                "role": "user",
                "content": "Who are you?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "thinking_budget": 50,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Response

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"Okay","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":14,"output_tokens":3,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":1}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":",","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":15,"output_tokens":4,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":2}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

......

id:133
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"!","reasoning_content":"","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":149,"output_tokens":138,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":50}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

id:134
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":149,"output_tokens":138,"input_tokens":11,"output_tokens_details":{"reasoning_tokens":50}},"request_id":"2ce91085-3602-9c32-9c8b-fe3d583a2c38"}

Sample code

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Configurations vary by region. Modify this based on your actual region.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# First turn of the conversation
messages = [
    {"role": "user", "content": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."}
]

first_reasoning = ""
first_content = ""
is_answering = False

completion = client.chat.completions.create(
    model="qwen3.7-plus",
    messages=messages,
    extra_body={"enable_thinking": True},
    stream=True,
    stream_options={"include_usage": True},
)

print("=" * 20 + "First-turn thought process" + "=" * 20)

for chunk in completion:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
        first_reasoning += delta.reasoning_content
        if not is_answering:
            print(delta.reasoning_content, end="", flush=True)
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "First-turn response" + "=" * 20)
            is_answering = True
        print(delta.content, end="", flush=True)
        first_content += delta.content

# Second turn: Pass the thought process and ask why the model excluded Kafka
messages = [
    {"role": "user", "content": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."},
    {
        "role": "assistant",
        "content": first_content,
        "reasoning_content": first_reasoning,
    },
    {"role": "user", "content": "Why did you exclude Kafka in your comparison?"},
]

reasoning_content = ""
answer_content = ""
is_answering = False

# Pass preserve_thinking through extra_body
completion = client.chat.completions.create(
    model="qwen3.7-plus",
    messages=messages,
    extra_body={
        "enable_thinking": True,
        "preserve_thinking": True,
    },
    stream=True,
    stream_options={"include_usage": True},
)

print("\n" + "=" * 20 + "Second-turn thought process" + "=" * 20)

for chunk in completion:
    if not chunk.choices:
        continue
    delta = chunk.choices[0].delta
    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
    if hasattr(delta, "content") and delta.content:
        if not is_answering:
            print("\n" + "=" * 20 + "Second-turn response" + "=" * 20)
            is_answering = True
        print(delta.content, end="", flush=True)
        answer_content += delta.content

Response

====================First-turn thought process====================
The user needs a message queue for an E-commerce system with tens of millions of daily messages. I will compare mainstream solutions based on dimensions such as throughput, reliability, delayed messages, and transaction support...

RocketMQ: Validated in Alibaba's E-commerce scenarios, natively supports transactional and delayed messages, and provides strict partition-level ordering...
Kafka: Extremely high throughput, but lacks native support for transactional and delayed messages, requiring custom compensation mechanisms...
RabbitMQ: Low latency, but limited cluster scalability, with a peak TPS in the tens of thousands...
====================First-turn response====================
Considering the core needs of an E-commerce scenario (transactional messages, delayed messages, ordering, and peak handling), I recommend Apache RocketMQ. If your team already has a Kafka ecosystem or requires strong real-time analytics, Kafka is also a viable option.
====================Second-turn thought process====================
The user is asking why Kafka was excluded. Reviewing my historical thought process, I did not exclude Kafka; I gave it a 4-star rating. I will refer to my previous detailed comparison to explain...

In the last turn, I compared the differences between RocketMQ and Kafka regarding transactional messages, delayed messages, and ordering. Kafka's main disadvantage is that it requires additional architectural design to compensate for E-commerce-specific semantics...
====================Second-turn response====================
I did not exclude Kafka. Kafka excels in throughput and ecosystem. The reason RocketMQ received a slightly higher rating is its better out-of-the-box match for core E-commerce workflows. RocketMQ natively supports transactional and delayed messages, while Kafka requires self-implementation through architectural patterns like the Outbox Pattern. If your team already has a Kafka ecosystem, it is fully capable of handling a scenario with tens of millions of messages.

Sample code

import OpenAI from "openai";
import process from 'process';

const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY,
    // Configurations vary by region. Modify this based on your actual region.
    baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
});

async function main() {
    // First turn of the conversation
    let firstReasoning = '';
    let firstContent = '';
    let isAnswering = false;

    const stream1 = await openai.chat.completions.create({
        model: 'qwen3.7-plus',
        messages: [{ role: 'user', content: 'I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation.' }],
        stream: true,
        enable_thinking: true
    });

    console.log('='.repeat(20) + 'First-turn thought process' + '='.repeat(20));

    for await (const chunk of stream1) {
        if (!chunk.choices?.length) continue;
        const delta = chunk.choices[0].delta;
        if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
            firstReasoning += delta.reasoning_content;
            if (!isAnswering) process.stdout.write(delta.reasoning_content);
        }
        if (delta.content !== undefined && delta.content) {
            if (!isAnswering) {
                console.log('\n' + '='.repeat(20) + 'First-turn response' + '='.repeat(20));
                isAnswering = true;
            }
            process.stdout.write(delta.content);
            firstContent += delta.content;
        }
    }

    // Second turn: Pass the thought process
    let reasoningContent = '';
    let answerContent = '';
    isAnswering = false;

    const stream2 = await openai.chat.completions.create({
        model: 'qwen3.7-plus',
        messages: [
            { role: 'user', content: 'I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation.' },
            {
                role: 'assistant',
                content: firstContent,
                reasoning_content: firstReasoning
            },
            { role: 'user', content: 'Why did you exclude Kafka in your comparison?' }
        ],
        stream: true,
        enable_thinking: true,
        // Pass preserve_thinking as a top-level parameter
        preserve_thinking: true
    });

    console.log('\n' + '='.repeat(20) + 'Second-turn thought process' + '='.repeat(20));

    for await (const chunk of stream2) {
        if (!chunk.choices?.length) continue;
        const delta = chunk.choices[0].delta;
        if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
            if (!isAnswering) process.stdout.write(delta.reasoning_content);
            reasoningContent += delta.reasoning_content;
        }
        if (delta.content !== undefined && delta.content) {
            if (!isAnswering) {
                console.log('\n' + '='.repeat(20) + 'Second-turn response' + '='.repeat(20));
                isAnswering = true;
            }
            process.stdout.write(delta.content);
            answerContent += delta.content;
        }
    }
}

main();

Response

====================First-turn thought process====================
The user needs a message queue for an E-commerce system with tens of millions of daily messages. I will compare mainstream solutions based on dimensions such as throughput, reliability, delayed messages, and transaction support...
====================First-turn response====================
Considering the core needs of an E-commerce scenario, I recommend Apache RocketMQ. If your team already has a Kafka ecosystem, Kafka is also a viable option.
====================Second-turn thought process====================
The user is asking why Kafka was excluded. Referring to my previous thought process, I did not exclude Kafka...
====================Second-turn response====================
I did not exclude Kafka. Kafka excels in throughput and ecosystem. The reason RocketMQ received a slightly higher rating is its better out-of-the-box match for core E-commerce workflows.

Sample code

# The base_url varies by region. Modify it based on your actual region.
curl -X POST https://{WorkspaceId}.ap-southeast-1.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": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."
        },
        {
            "role": "assistant",
            "content": "Considering the core needs of an E-commerce scenario, I recommend Apache RocketMQ.",
            "reasoning_content": "The user needs a message queue for an E-commerce system with tens of millions of daily messages. RocketMQ natively supports transactional and delayed messages, making it more suitable for E-commerce scenarios. Kafka has extremely high throughput but requires custom compensation mechanisms."
        },
        {
            "role": "user",
            "content": "Why did you exclude Kafka in your comparison?"
        }
    ],
    "stream": true,
    "stream_options": {
        "include_usage": true
    },
    "enable_thinking": true,
    "preserve_thinking": true
}'

Response

data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1743523200,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-example-001"}

.....

data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1743523200,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-example-001"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":3463,"completion_tokens":2387,"total_tokens":5850},"created":1743523200,"system_fingerprint":null,"model":"qwen3.7-plus","id":"chatcmpl-example-001"}

data: [DONE]

Sample code

import os
from dashscope import Generation
import dashscope

# The following URL is for the Singapore region. When calling, replace WorkspaceId with your actual workspace ID. URLs vary by region.
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/"

# First turn of the conversation
messages = [
    {"role": "user", "content": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."}
]

first_reasoning = ""
first_content = ""
is_answering = False

completion = Generation.call(
    # If the environment variable is not set, replace the next line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3.7-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    stream=True,
    incremental_output=True,
)

print("=" * 20 + "First-turn thought process" + "=" * 20)

for chunk in completion:
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
            first_reasoning += chunk.output.choices[0].message.reasoning_content
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "First-turn response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            first_content += chunk.output.choices[0].message.content

# Second turn: Pass the thought process
messages = [
    {"role": "user", "content": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."},
    {
        "role": "assistant",
        "content": first_content,
        "reasoning_content": first_reasoning,
    },
    {"role": "user", "content": "Why did you exclude Kafka in your comparison?"},
]

reasoning_content = ""
answer_content = ""
is_answering = False

completion = Generation.call(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="qwen3.7-plus",
    messages=messages,
    result_format="message",
    enable_thinking=True,
    # Pass the thought process
    preserve_thinking=True,
    stream=True,
    incremental_output=True,
)

print("\n" + "=" * 20 + "Second-turn thought process" + "=" * 20)

for chunk in completion:
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        if (
            chunk.output.choices[0].message.reasoning_content != ""
            and chunk.output.choices[0].message.content == ""
        ):
            print(chunk.output.choices[0].message.reasoning_content, end="", flush=True)
            reasoning_content += chunk.output.choices[0].message.reasoning_content
        elif chunk.output.choices[0].message.content != "":
            if not is_answering:
                print("\n" + "=" * 20 + "Second-turn response" + "=" * 20)
                is_answering = True
            print(chunk.output.choices[0].message.content, end="", flush=True)
            answer_content += chunk.output.choices[0].message.content

Response

====================First-turn thought process====================
The user needs a message queue for an E-commerce system with tens of millions of daily messages. I will compare mainstream solutions based on dimensions such as throughput, reliability, delayed messages, and transaction support...
====================First-turn response====================
Considering the core needs of an E-commerce scenario, I recommend Apache RocketMQ. If your team already has a Kafka ecosystem, Kafka is also a viable option.
====================Second-turn thought process====================
The user is asking why Kafka was excluded. Referring to my previous thought process, I did not exclude Kafka...
====================Second-turn response====================
I did not exclude Kafka. Kafka excels in throughput and ecosystem. The reason RocketMQ received a slightly higher rating is its better out-of-the-box match for core E-commerce workflows.

Sample code

# The base_url varies by region. Modify it based on your actual region.
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-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": "I need to choose a message queue for an E-commerce system that handles tens of millions of messages per day. Please provide a recommendation."
            },
            {
                "role": "assistant",
                "content": "Considering the core needs of an E-commerce scenario, I recommend Apache RocketMQ.",
                "reasoning_content": "The user needs a message queue for an E-commerce system with tens of millions of daily messages. RocketMQ natively supports transactional and delayed messages, making it more suitable for E-commerce scenarios. Kafka has extremely high throughput but requires custom compensation mechanisms."
            },
            {
                "role": "user",
                "content": "Why did you exclude Kafka in your comparison?"
            }
        ]
    },
    "parameters":{
        "enable_thinking": true,
        "preserve_thinking": true,
        "incremental_output": true,
        "result_format": "message"
    }
}'

Response

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"user"},"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":3466,"output_tokens":3,"input_tokens":3463,"output_tokens_details":{"reasoning_tokens":1}},"request_id":"example-request-001"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"follow-up"},"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":3467,"output_tokens":4,"input_tokens":3463,"output_tokens_details":{"reasoning_tokens":2}},"request_id":"example-request-001"}

......

id:200
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","reasoning_content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":5850,"output_tokens":2387,"input_tokens":3463,"output_tokens_details":{"reasoning_tokens":1347}},"request_id":"example-request-001"}