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Qwen3.7-Max

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Text GenerationReasoningVisual Understanding

Overview

Text GenerationReasoningVisual Understanding

The Max model, the largest and most capable in the Qwen3.7 series, has added visual‑modal understanding compared to the May 20 snapshot, enabling it to perceive real‑world scenes and supporting multimodal interactive hybrid agent capabilities. This version is based on a snapshot taken on June 8, 2026.

Input

ImageTextVideo

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

Web Search

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

feature.funeTuning

Pricing

  • Input
    $2.5Per 1M tokens
  • Output
    $7.5Per 1M tokens
  • Input(Implicit Cache)
    $0.5Per 1M tokens
  • Explicit Cache Creation
    $3.125Per 1M tokens
  • Explicit Cache Read
    $0.25Per 1M tokens

toc.rateLimitsAndContext

  • Max Input
    991.80K
  • Max Output
    65.53K
  • RPMRequests Per Minute
    60
  • TPMTokens Per Minute
    1M
  • contextField.maxInputThinking
    983.61K
  • contextField.maxOutputThinking
    65.53K
  • Context
    1M

Built-in Tools

code_interpreterResponses API
i2i_searchResponses API
t2i_searchResponses API
web_extractorResponses API
web_searchResponses API

API Reference

Get API Key
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import os
import dashscope
dashscope.base_http_api_url = "https://dashscope-intl.aliyuncs.com/api/v1"

messages = [
    {
        "role": "user",
        "content": [
            {"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"},
            {"text": "What is depicted in the image?"}]
    }]
response = dashscope.MultiModalConversation.call(
    api_key=os.getenv('DASHSCOPE_API_KEY'),
    model='qwen3.7-max-2026-06-08',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])