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    Alibaba Cloud Model Studio:Penalaran visual

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    Alibaba Cloud Model Studio:Penalaran visual

    Last Updated:Jun 02, 2026

    Model penalaran visual menghasilkan proses berpikirnya sebelum memberikan jawaban. Gunakan model ini untuk tugas visual kompleks seperti soal matematika, analisis grafik, atau pemahaman video.

    Showcase

    QVQ Logo
    Visual Reasoning
    Komponen di atas hanya untuk keperluan demonstrasi dan tidak mengirim permintaan yang sesungguhnya.

    Model yang didukung

    • Qwen3.7

      • Model hybrid-thinking: qwen3.7-plus, qwen3.7-plus-2026-05-26

    • Qwen3.6

      • Model hybrid-thinking: qwen3.6-plus, qwen3.6-plus-2026-04-02, qwen3.6-flash, qwen3.6-flash-2026-04-16, qwen3.6-35b-a3b

    • Qwen3.5

      • Model hybrid-thinking: qwen3.5-plus, qwen3.5-plus-2026-02-15, qwen3.5-flash, qwen3.5-flash-2026-02-23, qwen3.5-397b-a17b, qwen3.5-122b-a10b, qwen3.5-27b, qwen3.5-35b-a3b

    • Qwen3-VL

      • Model hybrid-thinking: qwen3-vl-plus, qwen3-vl-plus-2025-12-19, qwen3-vl-plus-2025-09-23, qwen3-vl-flash, qwen3-vl-flash-2025-10-15

      • Model thinking-only: qwen3-vl-235b-a22b-thinking, qwen3-vl-32b-thinking, qwen3-vl-30b-a3b-thinking, qwen3-vl-8b-thinking

    • QVQ

      • Model thinking-only: qvq-max series, qvq-plus series

    • Kimi

      • Model hybrid-thinking: kimi-k2.6, kimi-k2.5

    Panduan penggunaan

    • Proses berpikir: Model Studio menyediakan dua jenis model penalaran visual: hybrid-thinking dan thinking-only.

      • Model hybrid-thinking: Kendalikan proses berpikir dengan parameter enable_thinking:

        • Diatur ke true: menghasilkan proses berpikir terlebih dahulu, lalu respons akhir (default untuk seri Qwen3.6 dan Qwen3.5).

        • Diatur ke false: langsung menghasilkan respons (default untuk seri qwen3-vl-plus, qwen3-vl-flash).

      • Model thinking-only: Model ini selalu menghasilkan proses berpikir sebelum memberikan respons, dan perilaku ini tidak dapat dinonaktifkan.

    • Metode output: Gunakan streaming untuk mencegah timeout akibat proses berpikir yang panjang.

      • Qwen3.6, Qwen3.5, Qwen3-VL, kimi-k2.6, kimi-k2.5, dan stepfun/step-3.7-flash mendukung metode streaming dan non-streaming.

      • Seri QVQ hanya mendukung keluaran streaming.

    • Rekomendasi prompt sistem:

      • Konversasi single-turn/sederhana: Jangan atur System Message. Sampaikan instruksi (seperti role, format) melalui User Message untuk hasil inferensi terbaik.

      • Aplikasi kompleks (agen, pemanggilan tool): Gunakan System Message untuk menentukan peran model, kemampuan, dan kerangka perilaku.

    Memulai

    Prasyarat

    • Kunci API telah dibuat dan diekspor sebagai variabel lingkungan.

    • Pengguna SDK: instal versi terbaru (DashScope Python SDK ≥1.24.6, DashScope Java SDK ≥2.21.10).

    Contoh berikut memanggil qvq-max untuk menyelesaikan soal matematika dari gambar. Contoh ini menggunakan streaming untuk mencetak proses berpikir dan respons akhir secara terpisah.

    Kompatibel dengan OpenAI

    Python

    from openai import OpenAI
    import os
    
    # Initialize the OpenAI client
    client = OpenAI(
        # API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/api-key
        # If not configured, replace with: api_key="sk-xxx"
        api_key = os.getenv("DASHSCOPE_API_KEY"),
        # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
    )
    
    reasoning_content = ""  # Define the full thinking process
    answer_content = ""     # Define the full response
    is_answering = False   # Check if the thinking process has ended and the response has started
    
    # Create a chat completion request
    completion = client.chat.completions.create(
        model="qvq-max",  # Example uses qvq-max. Replace with other model names as needed.
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
                        },
                    },
                    {"type": "text", "text": "How do I solve this problem?"},
                ],
            },
        ],
        stream=True,
        # Uncomment the following to return token usage in the last chunk
        # stream_options={
        #     "include_usage": True
        # }
    )
    
    print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
    
    for chunk in completion:
        # If chunk.choices is empty, print the usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # Print the thinking process
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # Start responding
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
                    is_answering = True
                # Print the response process
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    
    # print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
    # print(reasoning_content)
    # print("=" * 20 + "Full response" + "=" * 20 + "\n")
    # print(answer_content)

    Node.js

    import OpenAI from "openai";
    import process from 'process';
    
    // Initialize the OpenAI client
    const openai = new OpenAI({
        apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable. API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/api-key
        // Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
    });
    
    let reasoningContent = '';
    let answerContent = '';
    let isAnswering = false;
    
    let messages = [
        {
            role: "user",
            content: [
            { type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
            { type: "text", text: "Solve this problem" },
        ]
    }]
    
    async function main() {
        try {
            const stream = await openai.chat.completions.create({
                model: 'qvq-max',
                messages: messages,
                stream: 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;
    
                // Handle the thinking process
                if (delta.reasoning_content) {
                    process.stdout.write(delta.reasoning_content);
                    reasoningContent += delta.reasoning_content;
                }
                // Handle the formal response
                else if (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();

    Klik untuk melihat proses berpikir dan respons lengkap

    ====================Thinking process====================
    
    Okay, I need to solve this problem about the surface area and volume of a rectangular prism and a cube. First, I need to carefully look at the problem to make sure I understand the requirements for each part.
    
    The problem asks to calculate the surface area and volume of two shapes separately. The first shape is a rectangular prism, and the second is a cube. The units are all in centimeters, and each question is worth 6 points, for a total of 12 points. This looks like a math homework problem, possibly from elementary or middle school geometry.
    
    First, I need to recall the formulas for the surface area and volume of a rectangular prism and a cube. For a rectangular prism, the formula for surface area is: \( 2(ab + bc + ac) \), where a, b, and c are the length, width, and height, respectively. The volume is \( abc \). For a cube, because all side lengths are equal, the surface area is \( 6a^2 \), and the volume is \( a^3 \).
    
    Now, let's look at the first shape, a rectangular prism with given dimensions of length 4 cm, width 3 cm, and height 2 cm. I need to calculate its surface area first. Plugging the values into the formula:
    
    Surface Area = \( 2(4×3 + 3×2 + 4×2) \)
            = \( 2(12 + 6 + 8) \)
            = \( 2×26 \)
            = 52 cm²
    
    Next is the volume, again plugging in the values:
    
    Volume = \( 4×3×2 \)
          = 24 cm³
    
    Then, for the second shape, a cube with a side length of 3 cm. The surface area is calculated as follows:
    
    Surface Area = \( 6×3^2 \)
            = \( 6×9 \)
            = 54 cm²
    
    And the volume is:
    
    Volume = \( 3^3 \)
          = 27 cm³
    
    However, I need to check for any potential mistakes. For example, in calculating the surface area of the rectangular prism, did I miscalculate the area of any face? Or did I make a mistake in the multiplication?
    
    For instance, the three different faces of the rectangular prism are 4×3, 3×2, and 4×2. The areas of these faces are 12, 6, and 8. Adding them up gives 26, and multiplying by 2 gives 52, which should be correct. For the volume, 4×3=12, and then multiplying by 2 is 24, which is also correct.
    
    For the cube, each face is 3×3=9, and with six faces, the total is 54. The volume is 3×3×3=27, which is also correct.
    
    So the final answers should be:
    
    1. The surface area of the rectangular prism is 52 square centimeters, and the volume is 24 cubic centimeters.
    2. The surface area of the cube is 54 square centimeters, and the volume is 27 cubic centimeters.
    
    However, I need to confirm if there is any other hidden information or potential traps in the figures in the problem. For example, sometimes problems might include dashed lines or shaded areas, but in this case, the rectangular prism and cube in the image look standard, with no special markings. Therefore, I believe my calculations are correct.
    
    ====================Full response====================
    
    ### Solution:
    
    **1. Rectangular Prism (length 4 cm, width 3 cm, height 2 cm)**
    - **Surface Area**:
      \[
      2 \times (4 \times 3 + 3 \times 2 + 4 \times 2) = 2 \times (12 + 6 + 8) = 2 \times 26 = 52 \, \text{cm}^2
      \]
    - **Volume**:
      \[
      4 \times 3 \times 2 = 24 \, \text{cm}^3
      \]
    
    **2. Cube (side length 3 cm)**
    - **Surface Area**:
      \[
      6 \times 3^2 = 6 \times 9 = 54 \, \text{cm}^2
      \]
    - **Volume**:
      \[
      3^3 = 27 \, \text{cm}^3
      \]
    
    **Answer:**
    1. The surface area of the rectangular prism is \(52 \, \text{cm}^2\), and its volume is \(24 \, \text{cm}^3\).
    2. The surface area of the cube is \(54 \, \text{cm}^2\), and its volume is \(27 \, \text{cm}^3\).
    

    HTTP

    # ======= IMPORTANT =======
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/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": "qvq-max",
        "messages": [
        {
          "role": "user",
          "content": [
            {
              "type": "image_url",
              "image_url": {
                "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
              }
            },
            {
              "type": "text",
              "text": "Solve this problem"
            }
          ]
        }
      ],
        "stream":true,
        "stream_options":{"include_usage":true}
    }'

    Klik untuk melihat proses berpikir dan respons lengkap

    data: {"choices":[{"delta":{"content":null,"role":"assistant","reasoning_content":""},"index":0,"logprobs":null,"finish_reason":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    
    data: {"choices":[{"finish_reason":null,"delta":{"content":null,"reasoning_content":"Okay"},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    
    data: {"choices":[{"delta":{"content":null,"reasoning_content":","},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    
    data: {"choices":[{"delta":{"content":null,"reasoning_content":" I am now"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    
    data: {"choices":[{"delta":{"content":null,"reasoning_content":" going to"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    
    data: {"choices":[{"delta":{"content":null,"reasoning_content":" solve"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983020,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-ab4f3963-2c2a-9291-bda2-65d5b325f435"}
    .....
    data: {"choices":[{"delta":{"content":"square "},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
    
    data: {"choices":[{"delta":{"content":"centimeters"},"finish_reason":null,"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
    
    data: {"choices":[{"finish_reason":"stop","delta":{"content":"","reasoning_content":null},"index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
    
    data: {"choices":[],"object":"chat.completion.chunk","usage":{"prompt_tokens":544,"completion_tokens":590,"total_tokens":1134,"completion_tokens_details":{"text_tokens":590},"prompt_tokens_details":{"text_tokens":24,"image_tokens":520}},"created":1742983095,"system_fingerprint":null,"model":"qvq-max","id":"chatcmpl-23d30959-42b4-9f24-b7ab-1bb0f72ce265"}
    
    data: [DONE]

    DashScope

    Catatan

    Model QVQ melalui DashScope:

    • incremental_output default-nya true (tidak dapat dinonaktifkan; hanya streaming).

    • result_format default-nya "message" (tidak dapat diatur ke "text").

    Python

    import os
    import dashscope
    from dashscope import MultiModalConversation
    
    # Replace {WorkspaceId} with your 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": [
                {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                {"text": "How do I solve this problem?"}
            ]
        }
    ]
    
    response = MultiModalConversation.call(
        # API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/api-key
        # 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="qvq-max",  # Example uses qvq-max. Replace with other model names as needed.
        messages=messages,
        stream=True,
    )
    
    # Define the full thinking process
    reasoning_content = ""
    # Define the full response
    answer_content = ""
    # Check if the thinking process has ended and the response has started
    is_answering = False
    
    print("=" * 20 + "Thinking process" + "=" * 20)
    
    for chunk in response:
        # If both the thinking process and the response are empty, ignore
        message = chunk.output.choices[0].message
        reasoning_content_chunk = message.get("reasoning_content", None)
        if (chunk.output.choices[0].message.content == [] and
            reasoning_content_chunk == ""):
            pass
        else:
            # If it is currently the thinking process
            if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
                print(chunk.output.choices[0].message.reasoning_content, end="")
                reasoning_content += chunk.output.choices[0].message.reasoning_content
            # If it is currently 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[0]["text"], end="")
                answer_content += chunk.output.choices[0].message.content[0]["text"]
    
    # To print the full 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}")

    Klik untuk melihat proses berpikir dan respons lengkap

    ====================Thinking process====================
    Okay, I need to solve this problem about the surface area and volume of a rectangular prism and a cube. First, I need to carefully look at the problem to make sure I understand the requirements for each part.
    
    The problem asks to calculate the surface area and volume of two shapes separately. The first shape is a rectangular prism, and the second is a cube. The units are all in centimeters, and each question is worth 6 points, for a total of 12 points. This looks like a math homework problem, possibly from elementary or middle school geometry.
    
    First, I need to recall the formulas for the surface area and volume of a rectangular prism and a cube. For a rectangular prism, the formula for surface area is: \( Area = 2(ab + bc + ac) \), where a, b, and c are the length, width, and height, respectively. The volume is: \( Volume = abc \). For a cube, because all side lengths are equal, the surface area is: \( Area = 6a^2 \), and the volume is: \( Volume = a^3 \).
    
    Now, let's look at the first shape, a rectangular prism with given dimensions of length 4 cm, width 3 cm, and height 2 cm. I need to first confirm that these values correspond correctly to the variables in the formula. Usually, the three dimensions of a rectangular prism can be named arbitrarily, but for convenience, we can consider the longest side as the length, the middle one as the width, and the shortest as the height. However, in this case, the problem has clearly labeled the lengths of each side, so I can use them directly.
    
    Next, I'll calculate the surface area of the first rectangular prism. Plugging the values into the formula:
    
    \( Area = 2(4×3 + 3×2 + 4×2) \)
    
    First, calculate each term inside the parentheses:
    
    \( 4×3 = 12 \)
    \( 3×2 = 6 \)
    \( 4×2 = 8 \)
    
    Then, add these results together:
    
    \( 12 + 6 + 8 = 26 \)
    
    And multiply by 2:
    
    \( 2 × 26 = 52 \)
    
    So, the surface area of the first rectangular prism is 52 square centimeters.
    
    Next, calculate the volume:
    
    \( Volume = 4 × 3 × 2 = 24 \)
    
    So, the volume is 24 cubic centimeters.
    
    Now, let's look at the second shape, a cube with all side lengths of 3 cm. Therefore, the surface area calculation is:
    
    \( Area = 6 × 3^2 = 6 × 9 = 54 \)
    
    And the volume is:
    
    \( Volume = 3^3 = 27 \)
    
    So, the surface area of the cube is 54 square centimeters, and the volume is 27 cubic centimeters.
    
    During the calculation, I need to pay attention to the consistency of units. The unit given in the problem is centimeters, so the final results should be in square centimeters and cubic centimeters. Also, I need to ensure there are no calculation errors, such as the order of multiplication and addition, especially when calculating the surface area of the rectangular prism, where it's easy to miss a term or make a calculation mistake.
    
    Additionally, I'll check for any other possible misunderstandings, such as whether the length, width, and height are correctly identified. But in this problem, because the lengths of each side are clearly labeled, this should not be an issue. Also, for the cube, all side lengths are equal, so there's no complexity from different side lengths.
    
    To summarize, the surface area of the first rectangular prism is 52 square centimeters, and its volume is 24 cubic centimeters. The surface area of the second cube is 54 square centimeters, and its volume is 27 cubic centimeters.
    
    ====================Full response====================
    ### Solution:
    
    **1. Rectangular Prism (length 4 cm, width 3 cm, height 2 cm)**
    
    - **Surface Area**:
      \[
      Area = 2(ab + bc + ac) = 2(4×3 + 3×2 + 4×2) = 2(12 + 6 + 8) = 2×26 = 52 \, \text{cm}^2
      \]
    
    - **Volume**:
      \[
      Volume = abc = 4×3×2 = 24 \, \text{cm}^3
      \]
    
    **2. Cube (side length 3 cm)**
    
    - **Surface Area**:
      \[
      Area = 6a^2 = 6×3^2 = 6×9 = 54 \, \text{cm}^2
      \]
    
    - **Volume**:
      \[
      Volume = a^3 = 3^3 = 27 \, \text{cm}^3
      \]
    
    **Answer:**
    1. The surface area of the rectangular prism is \(52 \, \text{cm}^2\), and its volume is \(24 \, \text{cm}^3\).
    2. The surface area of the cube is \(54 \, \text{cm}^2\), and its volume is \(27 \, \text{cm}^3\).
    

    Java

    // DashScope SDK version >= 2.19.0
    import java.util.*;
    
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    import com.alibaba.dashscope.common.Role;
    import com.alibaba.dashscope.exception.ApiException;
    import com.alibaba.dashscope.exception.NoApiKeyException;
    import io.reactivex.Flowable;
    
    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.exception.UploadFileException;
    import com.alibaba.dashscope.exception.InputRequiredException;
    import java.lang.System;
    import com.alibaba.dashscope.utils.Constants;
    
    public class Main {
        static {
           // Replace {WorkspaceId} with your workspace ID. URLs vary by 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(MultiModalConversationResult message) {
            String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
            String reasoning = Objects.isNull(re)?"":re; // Default value
    
            List<Map<String, Object>> 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 (Objects.nonNull(content) && !content.isEmpty()) {
                Object text = content.get(0).get("text");
                finalContent.append(content.get(0).get("text"));
                if (!isFirstPrint) {
                    System.out.println("\n====================Full response====================");
                    isFirstPrint = true;
                }
                System.out.print(text);
            }
        }
        public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg)  {
            return MultiModalConversationParam.builder()
                    // API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/api-key
                    // If not configured, replace with: .apiKey("sk-xxx")
                    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                    // Example uses qvq-max. Replace with other model names as needed.
                    .model("qvq-max")
                    .messages(Arrays.asList(Msg))
                    .incrementalOutput(true)
                    .build();
        }
    
        public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
                throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
            MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
            Flowable<MultiModalConversationResult> result = conv.streamCall(param);
            result.blockingForEach(message -> {
                handleGenerationResult(message);
            });
        }
        public static void main(String[] args) {
            try {
                MultiModalConversation conv = new MultiModalConversation();
                MultiModalMessage userMsg = MultiModalMessage.builder()
                        .role(Role.USER.getValue())
                        .content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
                                Collections.singletonMap("text", "Solve this problem")))
                        .build();
                streamCallWithMessage(conv, userMsg);
    //             Print the final result
    //            if (reasoningContent.length() > 0) {
    //                System.out.println("\n====================Full response====================");
    //                System.out.println(finalContent.toString());
    //            }
            } catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
                logger.error("An exception occurred: {}", e.getMessage());
            }
            System.exit(0);
        }
    }

    Klik untuk melihat proses berpikir dan respons lengkap

    ====================Thinking process====================
    Hmm, I need to solve this problem, which is to calculate the surface area and volume of two shapes. First, I need to carefully examine the image provided in the problem. The first shape is a rectangular prism, and the second is a cube. The problem asks to calculate their surface area and volume separately, with units in centimeters.
    
    Let's look at the first shape, the rectangular prism. Its dimensions should be length, width, and height. According to the labels on the image, the length is 4 cm, the width is 3 cm, and the height is 2 cm. Right? I remember the formula for the surface area of a rectangular prism is 2 times (length × width + length × height + width × height). And the volume is length times width times height. Let me double-check if the formula is correct. Yes, the surface area is indeed the sum of the areas of the six faces, and because opposite faces have equal areas, this formula is correct.
    
    So, plugging in the values, the surface area should be 2×(4×3 + 4×2 + 3×2). First, calculate the terms inside the parentheses: 4×3=12, 4×2=8, 3×2=6. Adding them up gives 12+8+6=26. Then multiplying by 2 gives 52 square centimeters. For the volume, 4×3×2=24 cubic centimeters. This part should be correct.
    
    Next is the second shape, the cube. All its side lengths are 3 cm. The surface area of a cube is 6 times the square of the side length, because it has six identical square faces. The volume is the cube of the side length. So the surface area should be 6×3²=6×9=54 square centimeters. The volume is 3³=27 cubic centimeters. I need to pay attention to the units here. The problem states the unit is cm, so the results should be written in square centimeters and cubic centimeters.
    
    However, I should double-check if I made any mistakes. For example, are the sides of the rectangular prism correctly identified? In the image, the length of the rectangular prism does look longer than its width, so length is 4, width is 3, and height is 2. For the cube, all three dimensions are 3, which is fine. Did I make any calculation errors? For example, in the surface area calculation for the rectangular prism, are the products correct, and is the addition correct? For instance, 4×3=12, 4×2=8, 3×2=6, adding up to 26, and multiplying by 2 is 52, which is correct. The volume 4×3×2=24 is also correct. For the cube, the surface area 6×9=54 and volume 27 are also correct.
    
    One thing to note is the units. The problem clearly states the unit is cm, so I should add the correct unit symbols to the answers. Also, the problem states that each question is worth 6 points, for a total of 12 points, but there are only two questions, so each is worth 6 points. This doesn't affect the calculation process, but it's a reminder not to miss any steps or units.
    
    To summarize, the surface area of the first shape is 52 square centimeters, and its volume is 24 cubic centimeters; the surface area of the second shape is 54 square centimeters, and its volume is 27 cubic centimeters. That should be it.
    
    ====================Full response====================
    **Answer:**
    
    1. **Rectangular Prism**  
       - **Surface Area**: \(2 \times (4 \times 3 + 4 \times 2 + 3 \times 2) = 2 \times 26 = 52\) square centimeters  
       - **Volume**: \(4 \times 3 \times 2 = 24\) cubic centimeters  
    
    2. **Cube**  
       - **Surface Area**: \(6 \times 3^2 = 6 \times 9 = 54\) square centimeters  
       - **Volume**: \(3^3 = 27\) cubic centimeters  
    
    **Explanation:**  
    - The surface area of a rectangular prism is obtained by calculating the total area of its six faces, and its volume is the product of its length, width, and height.  
    - The surface area of a cube is the sum of the areas of its six identical square faces, and its volume is the cube of its side length.  
    - All units are in centimeters, as required by the problem.

    HTTP

    curl

    # ======= IMPORTANT =======
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # API keys differ by region. To obtain one, see https://bailian.console.alibabacloud.com/?tab=model#/api-key
    # === 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": "qvq-max",
        "input":{
            "messages":[
                {
                    "role": "user",
                    "content": [
                        {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                        {"text": "Solve this problem"}
                    ]
                }
            ]
        }
    }'

    Klik untuk melihat proses berpikir dan respons lengkap

    id:1
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":"Okay","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":547,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":3,"input_tokens":544,"output_tokens_details":{"text_tokens":3},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
    
    id:2
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":",","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":548,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":4,"input_tokens":544,"output_tokens_details":{"text_tokens":4},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
    
    id:3
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[],"reasoning_content":" I am now","role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":549,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":5,"input_tokens":544,"output_tokens_details":{"text_tokens":5},"image_tokens":520},"request_id":"f361ae45-fbef-9387-9f35-1269780e0864"}
    .....
    id:566
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[{"text":"square"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":1132,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":588,"input_tokens":544,"output_tokens_details":{"text_tokens":588},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}
    
    id:567
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[{"text":"centimeters"}],"role":"assistant"},"finish_reason":"null"}]},"usage":{"total_tokens":1133,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":589,"input_tokens":544,"output_tokens_details":{"text_tokens":589},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}
    
    id:568
    event:result
    :HTTP_STATUS/200
    data:{"output":{"choices":[{"message":{"content":[],"role":"assistant"},"finish_reason":"stop"}]},"usage":{"total_tokens":1134,"input_tokens_details":{"image_tokens":520,"text_tokens":24},"output_tokens":590,"input_tokens":544,"output_tokens_details":{"text_tokens":590},"image_tokens":520},"request_id":"758b0356-653b-98ac-b4d3-f812437ba1ec"}

    Kemampuan inti

    Mengaktifkan atau menonaktifkan proses berpikir

    Untuk skenario yang memerlukan proses berpikir mendetail (pemecahan masalah, analisis laporan), aktifkan mode berpikir menggunakan parameter enable_thinking seperti ditunjukkan di bawah.

    Kompatibel dengan OpenAI

    enable_thinking dan thinking_budget adalah parameter non-standar OpenAI. Metode pengiriman parameter bervariasi berdasarkan bahasa:

    • Python SDK: Anda harus mengirimkannya melalui dictionary extra_body.

    • Node.js SDK: Anda dapat mengirimkannya langsung sebagai parameter tingkat atas.

    import os
    from openai import OpenAI
    
    client = OpenAI(
        # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        # If you are using a model in the 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"
    )
    
    reasoning_content = ""  # Define the full thinking process
    answer_content = ""     # Define the full response
    is_answering = False   # Check if the thinking process has ended and the response has started
    enable_thinking = True
    # Create a chat completion request
    completion = client.chat.completions.create(
        model="qwen3.5-plus",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
                        },
                    },
                    {"type": "text", "text": "How do I solve this problem?"},
                ],
            },
        ],
        stream=True,
        # The enable_thinking parameter enables the thinking process. The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
        # For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
        extra_body={
            'enable_thinking': enable_thinking
            },
    
        # Uncomment the following to return token usage in the last chunk
        # stream_options={
        #     "include_usage": True
        # }
    )
    
    if enable_thinking:
        print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
    
    for chunk in completion:
        # If chunk.choices is empty, print the usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # Print the thinking process
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # Start responding
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
                    is_answering = True
                # Print the response process
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    
    # print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
    # print(reasoning_content)
    # print("=" * 20 + "Full response" + "=" * 20 + "\n")
    # print(answer_content)
    import OpenAI from "openai";
    
    // Initialize the OpenAI client
    const openai = new OpenAI({
      // API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
      // If no environment variable configured: apiKey: "sk-xxx"
      apiKey: process.env.DASHSCOPE_API_KEY,
     // Replace {WorkspaceId} with your workspace ID. URLs vary by region.
     //  If you are using a model in the 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"
    });
    
    let reasoningContent = '';
    let answerContent = '';
    let isAnswering = false;
    let enableThinking = true;
    
    let messages = [
        {
            role: "user",
            content: [
            { type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
            { type: "text", text: "Solve this problem" },
        ]
    }]
    
    async function main() {
        try {
            const stream = await openai.chat.completions.create({
                model: 'qwen3.5-plus',
                messages: messages,
                stream: true,
              // Note: In Node.js SDK, non-standard parameters (like enableThinking) pass as top-level properties, not in extra_body.
              enable_thinking: enableThinking
    
            });
    
            if (enableThinking){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;
    
                // Handle the thinking process
                if (delta.reasoning_content) {
                    process.stdout.write(delta.reasoning_content);
                    reasoningContent += delta.reasoning_content;
                }
                // Handle the formal response
                else if (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();
    # ======= IMPORTANT =======
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # If you are using a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
    # API keys differ by region. To obtain an API key, see 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": "qwen3.5-plus",
        "messages": [
        {
          "role": "user",
          "content": [
            {
              "type": "image_url",
              "image_url": {
                "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
              }
            },
            {
              "type": "text",
              "text": "Solve this problem"
            }
          ]
        }
      ],
        "stream":true,
        "stream_options":{"include_usage":true},
        "enable_thinking": true
    }'

    DashScope

    import os
    import dashscope
    from dashscope import MultiModalConversation
    
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # If you are using a model in the 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"
    
    enable_thinking = True
    
    messages = [
        {
            "role": "user",
            "content": [
                {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                {"text": "How do I solve this problem?"}
            ]
        }
    ]
    
    response = MultiModalConversation.call(
        # If not configured, replace with: api_key="sk-xxx",
        # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv('DASHSCOPE_API_KEY'),
        model="qwen3.5-plus",  
        messages=messages,
        stream=True,
        # The enable_thinking parameter enables the thinking process.
        # For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
        enable_thinking=enable_thinking
    
    )
    
    # Define the full thinking process
    reasoning_content = ""
    # Define the full response
    answer_content = ""
    # Check if the thinking process has ended and the response has started
    is_answering = False
    
    if enable_thinking:
        print("=" * 20 + "Thinking process" + "=" * 20)
    
    for chunk in response:
        # If both the thinking process and the response are empty, ignore
        message = chunk.output.choices[0].message
        reasoning_content_chunk = message.get("reasoning_content", None)
        if (chunk.output.choices[0].message.content == [] and
            reasoning_content_chunk == ""):
            pass
        else:
            # If it is currently the thinking process
            if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
                print(chunk.output.choices[0].message.reasoning_content, end="")
                reasoning_content += chunk.output.choices[0].message.reasoning_content
            # If it is currently 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[0]["text"], end="")
                answer_content += chunk.output.choices[0].message.content[0]["text"]
    
    # To print the full 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}")
    // DashScope SDK version >= 2.21.10
    import java.util.*;
    
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    import com.alibaba.dashscope.common.Role;
    import com.alibaba.dashscope.exception.ApiException;
    import com.alibaba.dashscope.exception.NoApiKeyException;
    import io.reactivex.Flowable;
    
    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.exception.UploadFileException;
    import com.alibaba.dashscope.exception.InputRequiredException;
    import java.lang.System;
    import com.alibaba.dashscope.utils.Constants;
    
    public class Main {
        // Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        // If you are using a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
        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(MultiModalConversationResult message) {
            String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
            String reasoning = Objects.isNull(re)?"":re; // Default value
    
            List<Map<String, Object>> 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 (Objects.nonNull(content) && !content.isEmpty()) {
                Object text = content.get(0).get("text");
                finalContent.append(content.get(0).get("text"));
                if (!isFirstPrint) {
                    System.out.println("\n====================Full response====================");
                    isFirstPrint = true;
                }
                System.out.print(text);
            }
        }
        public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg)  {
            return MultiModalConversationParam.builder()
                    // If not configured, replace with: .apiKey("sk-xxx")
                    // API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                    .model("qwen3.5-plus")
                    .messages(Arrays.asList(Msg))
                    .enableThinking(true)
                    .incrementalOutput(true)
                    .build();
        }
    
        public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
                throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
            MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
            Flowable<MultiModalConversationResult> result = conv.streamCall(param);
            result.blockingForEach(message -> {
                handleGenerationResult(message);
            });
        }
        public static void main(String[] args) {
            try {
                MultiModalConversation conv = new MultiModalConversation();
                MultiModalMessage userMsg = MultiModalMessage.builder()
                        .role(Role.USER.getValue())
                        .content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
                                Collections.singletonMap("text", "Solve this problem")))
                        .build();
                streamCallWithMessage(conv, userMsg);
    //             Print the final result
    //            if (reasoningContent.length() > 0) {
    //                System.out.println("\n====================Full response====================");
    //                System.out.println(finalContent.toString());
    //            }
            } catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
                logger.error("An exception occurred: {}", e.getMessage());
            }
            System.exit(0);
        }
    }
    # ======= IMPORTANT =======
    # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # If you are using a model in the 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": "qwen3.5-plus",
        "input":{
            "messages":[
                {
                    "role": "user",
                    "content": [
                        {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                        {"text": "Solve this problem"}
                    ]
                }
            ]
        },
        "parameters":{
            "enable_thinking": true,
            "incremental_output": true
        }
    }'

    Batasi panjang proses berpikir

    Gunakan parameter thinking_budget untuk membatasi panjang token proses berpikir. Jika melebihi batas, konten akan dipotong dan model segera menghasilkan jawaban akhir. Nilai default-nya adalah panjang maksimum rantai-pikiran model. Untuk informasi lebih lanjut, lihat Daftar model.

    Penting

    Parameter thinking_budget didukung oleh Qwen3.6, Qwen3.5, Qwen3-VL (mode berpikir), kimi-k2.5 (mode berpikir), dan kimi-k2.6 (mode berpikir).

    Kompatibel dengan OpenAI

    thinking_budget adalah parameter non-standar OpenAI. Saat menggunakan OpenAI Python SDK, kirimkan melalui extra_body.

    import os
    from openai import OpenAI
    
    client = OpenAI(
        # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        # If you are using a model in the 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"
    )
    
    reasoning_content = ""  # Define the full thinking process
    answer_content = ""     # Define the full response
    is_answering = False   # Check if the thinking process has ended and the response has started
    enable_thinking = True
    # Create a chat completion request
    completion = client.chat.completions.create(
        model="qwen3.5-plus",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
                        },
                    },
                    {"type": "text", "text": "How do I solve this problem?"},
                ],
            },
        ],
        stream=True,
        # The enable_thinking parameter enables the thinking process. The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
        # For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
        extra_body={
            'enable_thinking': enable_thinking,
            "thinking_budget": 81920},
    
        # Uncomment the following to return token usage in the last chunk
        # stream_options={
        #     "include_usage": True
        # }
    )
    
    if enable_thinking:
        print("\n" + "=" * 20 + "Thinking process" + "=" * 20 + "\n")
    
    for chunk in completion:
        # If chunk.choices is empty, print the usage
        if not chunk.choices:
            print("\nUsage:")
            print(chunk.usage)
        else:
            delta = chunk.choices[0].delta
            # Print the thinking process
            if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
                print(delta.reasoning_content, end='', flush=True)
                reasoning_content += delta.reasoning_content
            else:
                # Start responding
                if delta.content != "" and is_answering is False:
                    print("\n" + "=" * 20 + "Full response" + "=" * 20 + "\n")
                    is_answering = True
                # Print the response process
                print(delta.content, end='', flush=True)
                answer_content += delta.content
    
    # print("=" * 20 + "Full thinking process" + "=" * 20 + "\n")
    # print(reasoning_content)
    # print("=" * 20 + "Full response" + "=" * 20 + "\n")
    # print(answer_content)
    import OpenAI from "openai";
    
    // Initialize the OpenAI client
    const openai = new OpenAI({
      // API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
      // If no environment variable configured: apiKey: "sk-xxx"
      apiKey: process.env.DASHSCOPE_API_KEY,
      // Replace {WorkspaceId} with your workspace ID. URLs vary by region.
      // If you are using a model in the 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"
    });
    
    let reasoningContent = '';
    let answerContent = '';
    let isAnswering = false;
    let enableThinking = true;
    
    let messages = [
        {
            role: "user",
            content: [
            { type: "image_url", image_url: { "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg" } },
            { type: "text", text: "Solve this problem" },
        ]
    }]
    
    async function main() {
        try {
            const stream = await openai.chat.completions.create({
                model: 'qwen3.5-plus',
                messages: messages,
                stream: true,
              // Note: In Node.js SDK, non-standard parameters (like enableThinking) pass as top-level properties, not in extra_body.
              enable_thinking: enableThinking,
              thinking_budget: 81920
    
            });
    
            if (enableThinking){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;
    
                // Handle the thinking process
                if (delta.reasoning_content) {
                    process.stdout.write(delta.reasoning_content);
                    reasoningContent += delta.reasoning_content;
                }
                // Handle the formal response
                else if (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();
    # ======= IMPORTANT =======
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # If you are using a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions
    # API keys differ by region. To obtain an API key, see 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": "qwen3.5-plus",
        "messages": [
        {
          "role": "user",
          "content": [
            {
              "type": "image_url",
              "image_url": {
                "url": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"
              }
            },
            {
              "type": "text",
              "text": "Solve this problem"
            }
          ]
        }
      ],
        "stream":true,
        "stream_options":{"include_usage":true},
        "enable_thinking": true,
        "thinking_budget": 81920
    }'

    DashScope

    import os
    import dashscope
    from dashscope import MultiModalConversation
    
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region.
    # If you are using a model in the 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"
    
    enable_thinking = True
    
    messages = [
        {
            "role": "user",
            "content": [
                {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                {"text": "How do I solve this problem?"}
            ]
        }
    ]
    
    response = MultiModalConversation.call(
        # If not configured, replace with: api_key="sk-xxx",
        # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
        api_key=os.getenv('DASHSCOPE_API_KEY'),
        model="qwen3.5-plus",  
        messages=messages,
        stream=True,
        # The enable_thinking parameter enables the thinking process.
        # For qwen3.5-plus, qwen3-vl-plus, and qwen3-vl-flash, you can use enable_thinking to enable or disable thinking (qwen3.5-plus is enabled by default). For models with the 'thinking' suffix, such as qwen3-vl-235b-a22b-thinking, enable_thinking can only be set to true. This parameter does not apply to other Qwen-VL models.
        enable_thinking=enable_thinking,
        # The thinking_budget parameter sets the maximum number of tokens for the reasoning process.
        thinking_budget=81920,
    
    )
    
    # Define the full thinking process
    reasoning_content = ""
    # Define the full response
    answer_content = ""
    # Check if the thinking process has ended and the response has started
    is_answering = False
    
    if enable_thinking:
        print("=" * 20 + "Thinking process" + "=" * 20)
    
    for chunk in response:
        # If both the thinking process and the response are empty, ignore
        message = chunk.output.choices[0].message
        reasoning_content_chunk = message.get("reasoning_content", None)
        if (chunk.output.choices[0].message.content == [] and
            reasoning_content_chunk == ""):
            pass
        else:
            # If it is currently the thinking process
            if reasoning_content_chunk != None and chunk.output.choices[0].message.content == []:
                print(chunk.output.choices[0].message.reasoning_content, end="")
                reasoning_content += chunk.output.choices[0].message.reasoning_content
            # If it is currently 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[0]["text"], end="")
                answer_content += chunk.output.choices[0].message.content[0]["text"]
    
    # To print the full 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}")
    // DashScope SDK version >= 2.21.10
    import java.util.*;
    
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    import com.alibaba.dashscope.common.Role;
    import com.alibaba.dashscope.exception.ApiException;
    import com.alibaba.dashscope.exception.NoApiKeyException;
    import io.reactivex.Flowable;
    
    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.exception.UploadFileException;
    import com.alibaba.dashscope.exception.InputRequiredException;
    import java.lang.System;
    import com.alibaba.dashscope.utils.Constants;
    
    public class Main {
        // Replace {WorkspaceId} with your workspace ID. URLs vary by region.
        // If you are using a model in the Beijing region, replace the base_url with https://dashscope.aliyuncs.com/api/v1
        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(MultiModalConversationResult message) {
            String re = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
            String reasoning = Objects.isNull(re)?"":re; // Default value
    
            List<Map<String, Object>> 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 (Objects.nonNull(content) && !content.isEmpty()) {
                Object text = content.get(0).get("text");
                finalContent.append(content.get(0).get("text"));
                if (!isFirstPrint) {
                    System.out.println("\n====================Full response====================");
                    isFirstPrint = true;
                }
                System.out.print(text);
            }
        }
        public static MultiModalConversationParam buildMultiModalConversationParam(MultiModalMessage Msg)  {
            return MultiModalConversationParam.builder()
                    // If not configured, replace with: .apiKey("sk-xxx")
                    // API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
                    .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                    .model("qwen3.5-plus")
                    .messages(Arrays.asList(Msg))
                    .enableThinking(true)
                    .thinkingBudget(81920)
                    .incrementalOutput(true)
                    .build();
        }
    
        public static void streamCallWithMessage(MultiModalConversation conv, MultiModalMessage Msg)
                throws NoApiKeyException, ApiException, InputRequiredException, UploadFileException {
            MultiModalConversationParam param = buildMultiModalConversationParam(Msg);
            Flowable<MultiModalConversationResult> result = conv.streamCall(param);
            result.blockingForEach(message -> {
                handleGenerationResult(message);
            });
        }
        public static void main(String[] args) {
            try {
                MultiModalConversation conv = new MultiModalConversation();
                MultiModalMessage userMsg = MultiModalMessage.builder()
                        .role(Role.USER.getValue())
                        .content(Arrays.asList(Collections.singletonMap("image", "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"),
                                Collections.singletonMap("text", "Solve this problem")))
                        .build();
                streamCallWithMessage(conv, userMsg);
    //             Print the final result
    //            if (reasoningContent.length() > 0) {
    //                System.out.println("\n====================Full response====================");
    //                System.out.println(finalContent.toString());
    //            }
            } catch (ApiException | NoApiKeyException | UploadFileException | InputRequiredException e) {
                logger.error("An exception occurred: {}", e.getMessage());
            }
            System.exit(0);
        }
    }
    # ======= IMPORTANT =======
    # API keys differ by region. To obtain an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key
    # Replace {WorkspaceId} with your workspace ID. URLs vary by region. 
    # If you are using a model in the 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": "qwen3.5-plus",
        "input":{
            "messages":[
                {
                    "role": "user",
                    "content": [
                        {"image": "https://img.alicdn.com/imgextra/i1/O1CN01gDEY8M1W114Hi3XcN_!!6000000002727-0-tps-1024-406.jpg"},
                        {"text": "Solve this problem"}
                    ]
                }
            ]
        },
        "parameters":{
            "enable_thinking": true,
            "incremental_output": true,
            "thinking_budget": 81920
        }
    }'

    Contoh lainnya

    Model penalaran visual mendukung semua fitur pemahaman visual untuk skenario kompleks seperti:

    • Pemahaman multi-gambar

    • Pemahaman video

    • Pemrosesan gambar resolusi tinggi

    • Mengirim file lokal (encoding Base64 atau path file)

    Penagihan

    Total biaya = (Token input × Harga per token input) + (Token output × Harga per token output).

    • Proses berpikir (reasoning_content) dikenai biaya sebagai token output. Jika tidak ada output berpikir, harga mode non-berpikir berlaku.

    • Untuk perhitungan token gambar/video, lihat Pemahaman gambar dan video.

    Referensi API

    Untuk parameter input dan output, lihat Generasi teks.

    Kode error

    Jika pemanggilan model gagal dan mengembalikan pesan error, lihat Kode error untuk penyelesaian.

    Thank you! We've received your feedback.