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Qwen3.8-Max

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ReasoningVisual UnderstandingText Generation

Overview

ReasoningVisual UnderstandingText Generation

2.4-trillion-parameter MoE flagship delivering a comprehensive leap in coding and professional work. Autonomously codes and delivers complete projects spanning 10+ days. Handles hundreds of specialized tasks across legal, financial, design, and other professional domains, producing production-grade results end-to-end in a single conversation. Native visual understanding runs through the full cycle of planning, execution, and verification, enabling deep semantic analysis of ultra-long documents and extended video content. In long-horizon tasks, plans autonomously, iterates through closed feedback loops, and continuously evolves.

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
    $2Per 1M tokens
  • Output
    $6Per 1M tokens
  • Input(Implicit Cache)
    $0.25Per 1M tokens
  • Explicit Cache Creation
    $2.5Per 1M tokens
  • Explicit Cache Read
    $0.17Per 1M tokens

Rate Limits & Context

  • Max Input
    991.80K
  • Max Output
    131.07K
  • RPMRequests Per Minute
    15K
  • TPMTokens Per Minute
    2M
  • Max Input (Thinking)
    983.61K
  • Max Output (Thinking)
    131.07K
  • Context
    1M

Built-in Tools

code_interpreterResponses API
web_extractorResponses API
web_searchResponses API
t2i_searchResponses API
i2i_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='qwen3.8-max',
    messages=messages
)
print(response.output.choices[0].message.content[0]["text"])