Model Marketplace

Qwen3-Max

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Overview

A preview version of the Max model in the Qwen 3 series, achieving an effective integration of thinking and non-thinking modes. In thinking mode, there is a significant enhancement in capabilities such as intelligent agent programming, common-sense reasoning, and reasoning across mathematics, science, and general domains.

Input

Text

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
    $6Per 1M tokens
  • Input(Implicit Cache)
    $0.24Per 1M tokens
  • Input
    $1.2Per 1M tokens
  • Output
    $6Per 1M tokens
  • Input(Implicit Cache)
    $0.24Per 1M tokens

Rate Limits & Context

  • Max Input
    258K
  • Max Output
    65K
  • Max Input (Thinking)
    258K
  • Max Output (Thinking)
    32K
  • Context
    262K
  • Max Reasoning
    81K
  • TPMTokens Per Minute
    1M
  • RPMRequests Per Minute
    600

Built-in Tools

code_interpreterResponses API

API Reference

Call API
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import os
from openai import OpenAI

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://dashscope-intl.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
    model="qwen3-max-preview",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who are you?"},
    ],
    stream=True
)
for chunk in completion:
    print(chunk.choices[0].delta.content, end="", flush=True)