Streaming output for Qwen models

Reference, synced 2026-06-13.

flowchart TD
  n0["Models"]
  n1["Overview"]
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  n3["Solutions"]
  n4["Pricing"]
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  n6["Partners"]
  n7["Support"]
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  n0 --> n1
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  n2 --> n3
  n3 --> n4
  n4 --> n5
  n5 --> n6
  n6 --> n7
  n7 --> n8

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Empowering AI innovation for both enterprises and developers with Alibaba Cloud’s best-in-class Qwen models, AI-native apps, and AI solutions.

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Models

HappyHorse-1.0-T2V \ Cinematic creative generation, ultimate dynamic details Qwen3-VL-Plus \ Native VL, spatial reasoning, 1M-context video analysis Wan2.7-VideoEdit \ Supports both localized and global editing with prompt

Qwen3.6-Plus \ Native multimodal, 1M context, agentic coding Wan2.7-Image-Pro \ Interactive editing, long-text rendering, precise prompt following Qwen-Plus \ Balanced intelligence, efficient inference, production-ready performance

Qwen-Image-2.0 \ Professional infographics, exquisite photorealism Z-Image-Turbo \ Ultra-fast image generation, high throughput, cost-optimized inference Qwen3-Coder-Next \ Multi-turn tool interactions, future-ready development support

Wan2.7-T2V \ High-fidelity T2V, 15s duration, advanced camera control Wan2.7-I2V \ Cinematic I2V with emotional depth and visceral impact Wan2.7-R2V \ Up to 5 mixed image/video inputs and audio timbre cloning

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Stream model responses token by token for real-time output. Streaming reduces perceived latency and prevents server timeouts in chat applications and long text generation.

How it works

Streaming uses Server-Sent Events (SSE): the server establishes an HTTP persistent connection and pushes chunks as they're generated. The client renders chunks in real time, character by character. Non-streaming calls return all content at once.

Who are you?

Waiting ...

I am Qwen, an AI assistant developed by Alibaba Cloud. I can answer questions, provide information, and have conversations with you. How can I assist you?

⏱️ Wait time: 3 seconds

Stream Disabled

For reference only. No requests are actually sent.

This component is for reference only. No requests are actually sent.

Billing

Streaming billing is identical to non-streaming calls—based on input and output token counts.

If the request is interrupted, billing covers only output tokens generated before the stop request.

How to use

Important

Some models only support streaming calls: the open-source version of Qwen3, the commercial and open-source versions of QwQ, QVQ, and Qwen-Omni.

Step 1: Set the API key and select a region

You need to create and configure an API key and export it as an environment variable.

Setting the API key as an environment variable (DASHSCOPE_API_KEY) is more secure than hard-coding it in your code.

Step 2: Make a streaming request

OpenAI compatible

DashScope

How to enable

Set the stream parameter to true.

View token usage

By default, the OpenAI protocol omits token usage. To include it in the final data block, set stream_options={"include_usage": true}.

Python

Node.js

curl

python

import os
from openai import OpenAI

# 1. Preparation: Initialize the client.
client = OpenAI(
    # We recommend configuring the API key as an environment variable to avoid hard coding.
    api_key=os.environ["DASHSCOPE_API_KEY"],
    # API keys are region-specific. Make sure the base_url matches the region of your API key.
    # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
    # If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# 2. Make a streaming request.
completion = client.chat.completions.create(
    model="qwen-plus",
    messages=[\
        {"role": "system", "content": "You are a helpful assistant."},\
        {"role": "user", "content": "Introduce yourself."}\
    ],
    stream=True,
    stream_options={"include_usage": True}
)

# 3. Process the streaming response.
# Storing chunks in a list and then joining is more efficient than repeated string concatenation.
content_parts = []
print("AI: ", end="", flush=True)

for chunk in completion:
    if chunk.choices:
        content = chunk.choices[0].delta.content or ""
        print(content, end="", flush=True)
        content_parts.append(content)
    elif chunk.usage:
        print("\n--- Request Usage ---")
        print(f"Input Tokens: {chunk.usage.prompt_tokens}")
        print(f"Output Tokens: {chunk.usage.completion_tokens}")
        print(f"Total Tokens: {chunk.usage.total_tokens}")

full_response = "".join(content_parts)
# print(f"\n--- Full Response ---\n{full_response}")

Return result

plaintext

AI: Hello! I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can answer questions, create text such as stories, official documents, emails, and scripts, perform logical reasoning, write code, express opinions, and play games. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to let me know!
--- Request Usage ---
Input Tokens: 26
Output Tokens: 87
Total Tokens: 113

nodejs

import OpenAI from "openai";

async function main() {
    // 1. Preparation: Initialize the client.
    // We recommend configuring the API key as an environment variable to avoid hard coding.
    if (!process.env.DASHSCOPE_API_KEY) {
        throw new Error("Set the DASHSCOPE_API_KEY environment variable.");
    }
    // API keys are region-specific. Make sure the baseURL matches the region of your API key.
    // If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    const client = new OpenAI({
        apiKey: process.env.DASHSCOPE_API_KEY,
        baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
    });

    try {
        // 2. Make a streaming request.
        const stream = await client.chat.completions.create({
            model: "qwen-plus",
            messages: [\
                { role: "system", content: "You are a helpful assistant." },\
                { role: "user", content: "Introduce yourself." },\
            ],
            stream: true,
            // Purpose: Get the token usage for this request from the last chunk.
            stream_options: { include_usage: true },
        });

        // 3. Process the streaming response.
        const contentParts = [];
        process.stdout.write("AI: ");

        for await (const chunk of stream) {
            // The last chunk does not contain choices, but it contains usage information.
            if (chunk.choices && chunk.choices.length > 0) {
                const content = chunk.choices[0]?.delta?.content || "";
                process.stdout.write(content);
                contentParts.push(content);
            } else if (chunk.usage) {
                // The request is complete. Print the token usage.
                console.log("\n--- Request Usage ---");
                console.log(`Input Tokens: ${chunk.usage.prompt_tokens}`);
                console.log(`Output Tokens: ${chunk.usage.completion_tokens}`);
                console.log(`Total Tokens: ${chunk.usage.total_tokens}`);
            }
        }

        const fullResponse = contentParts.join("");
        // console.log(`\n--- Full Response ---\n${fullResponse}`);

    } catch (error) {
        console.error("Request failed:", error);
    }
}

main();

Results

plaintext

AI: Hello! I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can answer questions, create text such as stories, official documents, emails, and scripts, perform logical reasoning, write code, express opinions, and play games. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me at any time!
--- Request Usage ---
Input Tokens: 26
Output Tokens: 89
Total Tokens: 115

Request

curl

# ======= Important =======
# Make sure the DASHSCOPE_API_KEY environment variable is set.
# API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you use a model in the China (Beijing) region, replace the base URL with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# === Delete 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" \
--no-buffer \
-d '{
    "model": "qwen-plus",
    "messages": [\
        {"role": "user", "content": "Who are you?"}\
    ],
    "stream": true,
    "stream_options": {"include_usage": true}
}'

Response

The response follows the SSE protocol. Each line starting with data: represents a data block.

json

data: {"choices":[{"delta":{"content":"","role":"assistant"},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"finish_reason":null,"delta":{"content":"I am"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" a"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" large-scale"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" language model from Alibaba"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":" Cloud, and my name is Qwen"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"delta":{"content":"."},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[{"finish_reason":"stop","delta":{"content":""},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":22,"completion_tokens":17,"total_tokens":39},"created":1726132850,"system_fingerprint":null,"model":"qwen-plus","id":"chatcmpl-428b414f-fdd4-94c6-b179-8f576ad653a8"}

data: [DONE]

data:: The data payload of the message, which is usually a JSON-formatted string.
[DONE]: Indicates that the entire streaming response has ended.
How to enable

Enable streaming based on your method:

Python SDK: Set stream=True.
Java SDK: Use the streamCall interface.
cURL: Set header X-DashScope-SSE: enable.
Incremental output

The DashScope protocol supports both incremental and non-incremental streaming output.

Incremental(Recommended): Each chunk contains only new content. Set incremental_output to true.
Example: ["I love", "to eat", "apples"]
Non-incremental: Each chunk contains all previous content. Wastes bandwidth and increases client processing load. Set incremental_output to false.
Example: ["I ", "I like ", "I like apples"]
View token usage

Each data block includes real-time token usage information.

Python

Java

curl

python

import os
from http import HTTPStatus
import dashscope
from dashscope import Generation

# 1. Preparation: Configure the API key and region.
# We recommend configuring the API key as an environment variable to avoid hard coding.
try:
    dashscope.api_key = os.environ["DASHSCOPE_API_KEY"]
except KeyError:
    raise ValueError("Set the DASHSCOPE_API_KEY environment variable.")

# API keys are region-specific. Make sure the base_url matches the region of your API key.
# The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1"

# 2. Make a streaming request.
messages = [\
    {"role": "system", "content": "You are a helpful assistant."},\
    {"role": "user", "content": "Introduce yourself."},\
]

try:
    responses = Generation.call(
        model="qwen-plus",
        messages=messages,
        result_format="message",
        stream=True,
        # Key: Set to True to get incremental output for better performance.
        incremental_output=True,
    )

    # 3. Process the streaming response.
    content_parts = []
    print("AI: ", end="", flush=True)

    for resp in responses:
        if resp.status_code == HTTPStatus.OK:
            content = resp.output.choices[0].message.content
            print(content, end="", flush=True)
            content_parts.append(content)

            # Check if this is the last packet.
            if resp.output.choices[0].finish_reason == "stop":
                usage = resp.usage
                print("\n--- Request Usage ---")
                print(f"Input Tokens: {usage.input_tokens}")
                print(f"Output Tokens: {usage.output_tokens}")
                print(f"Total Tokens: {usage.total_tokens}")
        else:
            # Handle errors.
            print(
                f"\nRequest failed: request_id={resp.request_id}, code={resp.code}, message={resp.message}"
            )
            break

    full_response = "".join(content_parts)
    # print(f"\n--- Full Response ---\n{full_response}")

except Exception as e:
    print(f"An unknown error occurred: {e}")

Sample response

plaintext

AI: Hello! I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can help you answer questions and create text such as stories, official documents, emails, and scripts, perform logical reasoning, write code, express opinions, and play games. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me at any time!
--- Request Usage ---
Input Tokens: 26
Output Tokens: 91
Total Tokens: 117

java

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 io.reactivex.Flowable;
import io.reactivex.schedulers.Schedulers;

import java.util.Arrays;
import java.util.concurrent.CountDownLatch;
import com.alibaba.dashscope.protocol.Protocol;

public class Main {
    public static void main(String[] args) {
        // 1. Get the API key.
        String apiKey = System.getenv("DASHSCOPE_API_KEY");
        if (apiKey == null || apiKey.isEmpty()) {
            System.err.println("Set the DASHSCOPE_API_KEY environment variable.");
            return;
        }

        // 2. Initialize the Generation instance.
        // API keys are region-specific. Make sure the baseUrl matches the region of your API key.
        // The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
        // If you use a model in the China (Beijing) region, replace the baseUrl with: https://dashscope.aliyuncs.com/api/v1
        Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
        CountDownLatch latch = new CountDownLatch(1);

        // 3. Build the request parameters.
        GenerationParam param = GenerationParam.builder()
                .apiKey(apiKey)
                .model("qwen-plus")
                .messages(Arrays.asList(
                        Message.builder()
                                .role(Role.USER.getValue())
                                .content("Introduce yourself.")
                                .build()
                ))
                .resultFormat(GenerationParam.ResultFormat.MESSAGE)
                .incrementalOutput(true) // Enable incremental output for streaming.
                .build();
        // 4. Make a streaming call and process the response.
        try {
            Flowable<GenerationResult> result = gen.streamCall(param);
            StringBuilder fullContent = new StringBuilder();
            System.out.print("AI: ");
            result
                    .subscribeOn(Schedulers.io()) // The request is executed on an I/O thread.
                    .observeOn(Schedulers.computation()) // The response is processed on a computation thread.
                    .subscribe(
                            // onNext: Process each response chunk.
                            message -> {
                                String content = message.getOutput().getChoices().get(0).getMessage().getContent();
                                String finishReason = message.getOutput().getChoices().get(0).getFinishReason();
                                // Output the content.
                                System.out.print(content);
                                fullContent.append(content);
                                // When finishReason is not null, it indicates the last chunk. Output the usage information.
                                if (finishReason != null && !"null".equals(finishReason)) {
                                    System.out.println("\n--- Request Usage ---");
                                    System.out.println("Input Tokens: " + message.getUsage().getInputTokens());
                                    System.out.println("Output Tokens: " + message.getUsage().getOutputTokens());
                                    System.out.println("Total Tokens: " + message.getUsage().getTotalTokens());
                                }
                                System.out.flush(); // Immediately flush the output.
                            },
                            // onError: Handle errors.
                            error -> {
                                System.err.println("\nRequest failed: " + error.getMessage());
                                latch.countDown();
                            },
                            // onComplete: Callback for completion.
                            () -> {
                                System.out.println(); // New line.
                                // System.out.println("Full response: " + fullContent.toString());
                                latch.countDown();
                            }
                    );
            // The main thread waits for the asynchronous task to complete.
            latch.await();
            System.out.println("Program execution finished.");
        } catch (Exception e) {
            System.err.println("Request exception: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Return value

plaintext

AI: Hello! I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can help you answer questions and create text such as stories, official documents, emails, and scripts, perform logical reasoning, write code, express opinions, and play games. I support multiple languages, including but not limited to Chinese, English, German, French, and Spanish. If you have any questions or need help, feel free to ask me at any time!
--- Request Usage ---
Input Tokens: 26
Output Tokens: 91
Total Tokens: 117

Request

curl

# ======= Important =======
# Make sure the DASHSCOPE_API_KEY environment variable is set.
# API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you use a model in the China (Beijing) region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === Delete 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": "system",\
                "content": "You are a helpful assistant."\
            },\
            {\
                "role": "user",\
                "content": "Who are you?"\
            }\
        ]
    },
    "parameters": {
        "result_format": "message",
        "incremental_output":true
    }
}'

Response

The response follows the SSE format. Each message includes the following:

id: The data block number.
event: The event type, which is always result.
HTTP status code information.
data: The JSON-formatted data payload.

json

id:1
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"I am","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":27,"output_tokens":1,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:2
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":" Qwen","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":30,"output_tokens":4,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:3
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":", an Alibaba","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":33,"output_tokens":7,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

...

id:13
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":" or need help, feel free to","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":90,"output_tokens":64,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:14
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":" ask me!","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":92,"output_tokens":66,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

id:15
event:result
:HTTP_STATUS/200
data:{"output":{"choices":[{"message":{"content":"","role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":92,"output_tokens":66,"input_tokens":26,"prompt_tokens_details":{"cached_tokens":0}},"request_id":"d30a9914-ac97-9102-b746-ce0cb35e3fa2"}

For multimodal models

Multimodal models support images and audio in conversations. Streaming implementation differs from text models:

User message construction: Input includes multimodal content (images, audio) in addition to text.
DashScope SDK: Use MultiModalConversation interface (Python) or class (Java).

For multimodal models, see: Image and video understanding, Text extraction (Qwen-OCR), Audio understanding (Qwen3-Omni-Captioner), and Kimi. Qwen-Omni supports only streaming output. Because its output can contain multimodal content such as text or audio, the logic for parsing results differs from other models. For more information, see Omni-modal.

OpenAI compatible

DashScope

Python

Node.js

curl

python

from openai import OpenAI
import os

client = OpenAI(
    # API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
    # If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

completion = client.chat.completions.create(
    model="qwen3-vl-plus",  # You can replace this with other multimodal models and modify the messages accordingly.
    messages=[\
        {"role": "user",\
        "content": [{"type": "image_url",\
                    "image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},},\
                    {"type": "text", "text": "What scene is depicted in the image?"}]}],
    stream=True,
  # stream_options={"include_usage": True}
)
full_content = ""
print("Streaming output content:")
for chunk in completion:
    # If stream_options.include_usage is True, the choices field of the last chunk is an empty list and should be skipped. You can get the token usage from chunk.usage.
    if chunk.choices and chunk.choices[0].delta.content != "":
        full_content += chunk.choices[0].delta.content
        print(chunk.choices[0].delta.content)
print(f"Full content: {full_content}")

nodejs

import OpenAI from "openai";

const openai = new OpenAI(
    {
        // API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
        // If you have not configured an environment variable, replace the following line with your Model Studio API key: apiKey: "sk-xxx"
        apiKey: process.env.DASHSCOPE_API_KEY,
        // The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
        // If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/compatible-mode/v1
        baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
    }
);

const completion = await openai.chat.completions.create({
    model: "qwen3-vl-plus",  //  You can replace this with other multimodal models and modify the messages accordingly.
    messages: [\
        {role: "user",\
        content: [{"type": "image_url",\
                    "image_url": {"url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},},\
                    {"type": "text", "text": "What scene is depicted in the image?"}]}],
    stream: true,
    // stream_options: { include_usage: true },
});

let fullContent = ""
console.log("Streaming output content:")
for await (const chunk of completion) {
    // If stream_options.include_usage is true, the choices field of the last chunk is an empty array and should be skipped. You can get the token usage from chunk.usage.
    if (chunk.choices[0] && chunk.choices[0].delta.content != null) {
      fullContent += chunk.choices[0].delta.content;
      console.log(chunk.choices[0].delta.content);
    }
}
console.log(`Full output content: ${fullContent}`)

curl

# ======= Important =======
# If you use a model in the China (Beijing) region, replace the base URL with: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
# API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Delete this comment before execution ===

curl --location 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen3-vl-plus",
    "messages": [\
    {\
      "role": "user",\
      "content": [\
        {\
          "type": "image_url",\
          "image_url": {\
            "url": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"\
          }\
        },\
        {\
          "type": "text",\
          "text": "What scene is depicted in the image?"\
        }\
      ]\
    }\
  ],
    "stream":true,
    "stream_options":{"include_usage":true}
}'

Python

Java

curl

python

import os
from dashscope import MultiModalConversation
import dashscope
# The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
# If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'

messages = [\
    {\
        "role": "user",\
        "content": [\
            {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},\
            {"text": "What scene is depicted in the image?"}\
        ]\
    }\
]

responses = MultiModalConversation.call(
    # API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model='qwen3-vl-plus',  #  You can replace this with other multimodal models and modify the messages accordingly.
    messages=messages,
    stream=True,
    incremental_output=True)

full_content = ""
print("Streaming output content:")
for response in responses:
    if response["output"]["choices"][0]["message"].content:
        print(response.output.choices[0].message.content[0]['text'])
        full_content += response.output.choices[0].message.content[0]['text']
print(f"Full content: {full_content}")

java

import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;

import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import io.reactivex.Flowable;
import com.alibaba.dashscope.utils.Constants;

public class Main {
    static {
        // The following URL is for the Singapore region. Replace {WorkspaceId} with your actual workspace ID.
        // If you use a model in the China (Beijing) region, replace the base_url with: https://dashscope.aliyuncs.com/api/v1
        Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
    }
    public static void streamCall()
            throws ApiException, NoApiKeyException, UploadFileException {
        MultiModalConversation conv = new MultiModalConversation();
        // must create mutable map.
        MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
                .content(Arrays.asList(Collections.singletonMap("image", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"),
                        Collections.singletonMap("text", "What scene is depicted in the image?"))).build();
        MultiModalConversationParam param = MultiModalConversationParam.builder()
                // API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
                // If you have not configured an environment variable, replace the following line with your Model Studio API key: .apiKey("sk-xxx")
                .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                .model("qwen3-vl-plus")  //  You can replace this with other multimodal models and modify the messages accordingly.
                .messages(Arrays.asList(userMessage))
                .incrementalOutput(true)
                .build();
        Flowable<MultiModalConversationResult> result = conv.streamCall(param);
        result.blockingForEach(item -> {
            try {
                List<Map<String, Object>> content = item.getOutput().getChoices().get(0).getMessage().getContent();
                    // Check if content exists and is not empty.
                if (content != null &&  !content.isEmpty()) {
                    System.out.println(content.get(0).get("text"));
                    }
            } catch (Exception e){
                System.exit(0);
            }
        });
    }

    public static void main(String[] args) {
        try {
            streamCall();
        } catch (ApiException | NoApiKeyException | UploadFileException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}

curl

# ======= Important =======
# API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you use a model in the China (Beijing) region, replace the base URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation
# === Delete this comment before execution ===

curl -X POST https://{WorkspaceId}.ap-southeast-1.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-vl-plus",
    "input":{
        "messages":[\
            {\
                "role": "user",\
                "content": [\
                    {"image": "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg"},\
                    {"text": "What scene is depicted in the image?"}\
                ]\
            }\
        ]
    },
    "parameters": {
        "incremental_output": true
    }
}'

For thinking models

Thinking models return reasoning_content (thinking process) first, then content (response). Determine the stage by checking packet status.

For more information about thinking models, see Deep thinking, Image and video understanding, and Visual reasoning.

To implement streaming output for Qwen3-Omni-Flash (thinking mode), see Omni-modal.

OpenAI compatible

DashScope

The following shows the response format when you use the OpenAI Python SDK to call the qwen-plus model in thinking mode with streaming output:

plaintext

# Thinking phase
...
ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=None, reasoning_content='Cover all key points while')
ChoiceDelta(content=None, function_call=None, refusal=None, role=None, tool_calls=None, reasoning_content='being natural and fluent.')
# Responding phase
ChoiceDelta(content='Hello! I am **Q', function_call=None, refusal=None, role=None, tool_calls=None, reasoning_content=None)
ChoiceDelta(content='wen** (', function_call=None, refusal=None, role=None, tool_calls=None, reasoning_content=None)
...

If reasoning_content is not None and content is None, the model is in the thinking stage.
If reasoning_content is None and content is not None, the model is in the response stage.
If both are None, the stage is the same as that of the previous packet.

Python

Node.js

HTTP

Sample code

python

from openai import OpenAI
import os

# Initialize the OpenAI client.
client = OpenAI(
    # If you have not configured an environment variable, replace the following line with your Model Studio API key: api_key="sk-xxx"
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    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,
    # The enable_thinking parameter enables the thinking process. This parameter is not supported for the qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, and QwQ models.
    extra_body={"enable_thinking": True},
    stream=True,
    # stream_options={
    #     "include_usage": True
    # },
)

reasoning_content = ""  # Full thinking process
answer_content = ""  # Full response
is_answering = False  # Whether the responding 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

    # Received content, 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

Return result

plaintext

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

Okay, the user is asking "Who are you?". I need to provide an accurate and friendly answer. First, I must confirm my identity: Qwen, developed by the Tongyi Lab at Alibaba Group. Next, I should explain my main functions, such as answering questions, creating text, and logical reasoning. I need to maintain a friendly tone and avoid being too technical to make the user feel at ease. I should also avoid complex jargon to keep the answer simple and clear. Additionally, I might add some interactive elements, inviting the user to ask more questions to encourage further conversation. Finally, I'll check if I've missed any important information, such as my Chinese name "Tongyi Qianwen" and English name "Qwen", and my parent company and lab. I need to ensure the answer is comprehensive and meets the user's expectations.
====================Full response====================

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

Sample code

nodejs

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
    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({
            // You can replace this with other Qwen3 or QwQ models as needed.
            model: 'qwen-plus',
            messages,
            stream: true,
            // The enable_thinking parameter enables the thinking process. This parameter is not supported for the qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, and QwQ models.
            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 thinking content.
            if (delta.reasoning_content !== undefined && delta.reasoning_content !== null) {
                if (!isAnswering) {
                    process.stdout.write(delta.reasoning_content);
                }
                reasoningContent += delta.reasoning_content;
            }

            // Start replying after content is received.
            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();

Return result

plaintext

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

Okay, the user is asking "Who are you?". I need to answer with my identity. First, I should clearly state that I am Qwen, a large-scale language model developed by Alibaba Cloud. Next, I can mention my main functions, such as answering questions, creating text, and logical reasoning. I should also emphasize my multilingual support, including Chinese and English, so the user knows I can handle requests in different languages. Additionally, I might need to explain my application scenarios, such as helping with study, work, and daily life. However, the user's question is quite direct, so I should keep it concise. I also need to ensure a friendly tone and invite the user to ask further questions. I will check for any missing important information, such as my version or latest updates, but the user probably doesn't need that level of detail. Finally, I will confirm the answer is accurate and free of errors.
====================Full response====================

I am Qwen, a large-scale language model developed by the Tongyi Lab at Alibaba Group. I can perform various tasks such as answering questions, creating text, logical reasoning, and coding. I support multiple languages, including Chinese and English. If you have any questions or need help, feel free to let me know!

Sample code

curl

For Qwen3 open-source models, set enable_thinking to true to enable the thinking mode. The enable_thinking parameter has no effect on the qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, QwQ, models.

curl

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
}'

Return result

json

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]

The following shows the data format when you use the DashScope Python SDK to call the qwen-plus model in thinking mode:

plaintext

# Thinking phase
...
{"role": "assistant", "content": "", "reasoning_content": "informative, "}
{"role": "assistant", "content": "", "reasoning_content": "so the user finds it helpful."}
# Responding phase
{"role": "assistant", "content": "I am Qwen", "reasoning_content": ""}
{"role": "assistant", "content": ", developed by Tongyi Lab", "reasoning_content": ""}
...

If reasoning_content is not an empty string and content is an empty string, the model is in the thinking stage.
If reasoning_content is an empty string and content is not an empty string, the model is in the response stage.
If both are empty strings, the stage is the same as that of the previous packet.

Python

Java

HTTP

Sample code

python

import os
from dashscope import Generation
import dashscope
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 you have not configured an environment variable, replace the following line with your Model Studio API key: api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # You can replace this with other deep thinking models as needed.
    model="qwen-plus",
    messages=messages,
    result_format="message", # Open-source Qwen3 models only support "message". For a better experience, we recommend you set this parameter to "message" for other models as well.
    # Enable deep thinking. This parameter has no effect on the qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, and QwQ models.
    enable_thinking=True,
    stream=True,
    incremental_output=True, # Open-source Qwen3 models only support true. For a better experience, we recommend you set this parameter to true for other models as well.
)

# Define the complete thinking process.
reasoning_content = ""
# Define the complete response.
answer_content = ""
# Determine whether the thinking process is complete and the response is being generated.
is_answering = False

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

for chunk in completion:
    # If both the thinking process and the response are empty, do nothing.
    if (
        chunk.output.choices[0].message.content == ""
        and chunk.output.choices[0].message.reasoning_content == ""
    ):
        pass
    else:
        # If the current part is 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 part is 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 complete thinking process and response, uncomment and run the following code.
# print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(f"{answer_content}")

Return result

plaintext

====================Thinking process====================
Okay, the user is asking, "Who are you?" I need to answer this question. First, I must clarify my identity: Qwen, a large-scale language model developed by Alibaba Cloud. Next, I should explain my functions and purposes, such as answering questions, creating text, and logical reasoning. I should also emphasize my goal of being a helpful assistant to users, providing help and support.

When responding, I should maintain a conversational tone and avoid using technical jargon or complex sentence structures. I can use friendly expressions, like "Hello there! ~", to make the conversation more natural. I also need to ensure the information is accurate and does not omit key points, such as my developer, main functions, and application scenarios.

I should also consider potential follow-up questions from the user, such as specific application examples or technical details. So, I can subtly set up opportunities in my answer to guide the user to ask more questions. For example, by mentioning, "Whether it's a question about daily life or a professional field, I can do my best to help," which is both comprehensive and open-ended.

Finally, I will check if the response is fluent, without repetition or redundancy, ensuring it is concise and clear. I will also maintain a balance between being friendly and professional, so the user feels that I am both approachable and reliable.
====================Full response====================
Hello there! ~ I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create text, perform logical reasoning, write code, and more, all to provide help and support to users. Whether it's a question about daily life or a professional field, I can do my best to help. How can I assist you?

Sample code

java

// dashscope SDK version >= 2.19.4
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 {
        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====================Full response====================");
                isFirstPrint = true;
            }
            System.out.print(content);
        }
    }
    private static GenerationParam buildGenerationParam(Message userMsg) {
        return GenerationParam.builder()
                // If you have not configured an environment variable, replace the following line with your 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====================Full response====================");
//                System.out.println(finalContent.toString());
//            }
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            logger.error("An exception occurred: {}", e.getMessage());
        }
        System.exit(0);
    }
}

Return result

plaintext

====================Thinking process====================
Okay, the user is asking "Who are you?". I need to answer based on my previous settings. First, my role is Qwen, a large-scale language model from Alibaba Group. I need to keep my language conversational, simple, and easy to understand.

The user might be new to me or wants to confirm my identity. I should first directly answer who I am, then briefly explain my functions and uses, such as answering questions, creating text, and coding. I also need to mention my multilingual support so the user knows I can handle requests in different languages.

Also, according to the guidelines, I need to maintain a human-like personality, so my tone should be friendly, and I might use emojis to add a touch of warmth. I might also need to guide the user to ask further questions or use my functions, for example, by asking them what they need help with.

I need to be careful not to use complex jargon and avoid being long-winded. I will check for any missed key points, such as multilingual support and specific capabilities. I will ensure the answer meets all requirements, including being conversational and concise.
====================Full response====================
Hello! I am Qwen, a large-scale 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 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

curl

For hybrid thinking models, set enable_thinking to true to enable the thinking mode. The enable_thinking parameter has no effect on the qwen3-30b-a3b-thinking-2507, qwen3-235b-a22b-thinking-2507, QwQ, models.

curl

# ======= Important =======
# API keys for different regions are different. To obtain an API key, visit: https://www.alibabacloud.com/help/en/model-studio/get-api-key
# If you use a model in the China (Beijing) region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation
# === Delete 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"
    }
}'

Return Value

plaintext

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":" the 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":" is asking","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":" feel free to","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":" let me","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":" 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"}

Going live

Performance and resource management: Each streaming request maintains an HTTP persistent connection and consumes resources. Configure appropriate connection pool size and timeout. Monitor file descriptor usage in high-concurrency scenarios to prevent exhaustion.
Client-side rendering: Use ReadableStream and TextDecoderStream APIs to process and render SSE event streams smoothly.
Model monitoring:
- Key metrics: Monitor time to first token (TTFT), the core metric for measuring the streaming experience, along with the API error rate and average response time.
- Alerting settings: Set alerts for abnormal API error rates, especially for 4xx and 5xx errors.
Nginx proxy: If using Nginx as a reverse proxy, disable output buffering (proxy_buffering off) to ensure real-time streaming. Default buffering disrupts immediate data push to clients.

Error codes

If the model call fails and returns an error message, see Error codes for resolution.

FAQ

Q: Why does the returned data not include usage information?

A: By default, the OpenAI protocol does not return usage information. You can set the stream_options parameter to include usage information in the final packet.

Q: Does streaming output affect the model's response quality?

A: No, it does not. However, some models only support streaming output, and non-streaming output may cause timeout errors. We recommend using streaming output.

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Step 1: Set the API key and select a region

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Q: Does streaming output affect the model's response quality?

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Return result ​

Sample code ​

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Streaming output for Qwen models

Models

Models

GenAI Application

AI Service

AI Use Case

Overview

Why Alibaba Cloud

Customers and Insights

What's New

Products

Featured Products

Related Programs

Solutions

Pricing

Overview & Tools

Optimize your cost

Resources

Technical Resources

Training&Certification

Developer Hub

Partners

Marketplace

Find a Partner

Become a Partner

Support

Support & Professional Services

Contact us

Language

Locale

How it works

Billing

How to use

Step 1: Set the API key and select a region

Step 2: Make a streaming request

Return result

Results

Request

Response

Return value

Request

Response

For multimodal models

For thinking models

Sample code

Return result

Sample code

Return result

Sample code

Return result

Sample code

Return result

Sample code

Return result

Sample code

Return Value

Going live

Error codes

FAQ

Q: Why does the returned data not include usage information?

Q: Does streaming output affect the model's response quality?

Sales Support

Technical Support

Connect & Report Abuse