Qwen3.8-Max - QwenCloud

Model Marketplace

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

ModelsBuilt-in Tools

• 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

toc.rateLimitsAndContext

• Max Input

991.80K

• Max Output

131.07K

• RPMRequests Per Minute
15K
• TPMTokens Per Minute
2M
• contextField.maxInputThinking

983.61K

• contextField.maxOutputThinking

131.07K

• Context

Built-in Tools

code\_interpreterResponses API

web\_extractorResponses API

web\_searchResponses API

t2i\_searchResponses API

i2i\_searchResponses API

DashScopeOpenAI

PythonJavacURL

Python

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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"])

Highlights