Model Marketplace

QVQ-Max

Will be retired on October 10, 2026View announcement
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Overview

The QVQ visual reasoning model supports visual input and chain-of-thought output, demonstrating stronger capabilities in mathematics, programming, visual analysis, creation, and general tasks.

Input

TextImageVideo

Output

Text

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Pricing

  • Input
    $1.2Per 1M tokens
  • Output
    $4.8Per 1M tokens

Rate Limits & Context

  • Max Input
    106K
  • Max Output
    8K
  • Context
    131K
  • TPMTokens Per Minute
    100K
  • RPMRequests Per Minute
    60

API Reference

Call API
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from openai import OpenAI
import os

# Initialize the OpenAI client
client = OpenAI(
    # If the environment variable is not set, replace it with your Model Studio API key: api_key="sk-xxx"
    api_key = os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://maas.qwencloudapi.com/compatible-mode/v1"
)

reasoning_content = ""  # Full thinking process
answer_content = ""     # Full response
is_answering = False   # Indicates whether to end the thinking process and start the response

# Create a chat completion request
completion = client.chat.completions.create(
    model="qvq-max",  # qvq-max is used as an example. You can replace it with another model name 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 to solve this problem?"},
            ],
        },
    ],
    stream=True,
    # Uncomment the following code 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 the response
            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 + "Thinking process" + "=" * 20 + "\n")
# print(reasoning_content)
# print("=" * 20 + "Full response" + "=" * 20 + "\n")
# print(answer_content)