Model Marketplace

Qwen-Max

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Text Generation

Overview

Text Generation

Qwen-Max supports a parameter scale of hundreds of billions and multiple input languages such as Chinese and English. Qwen-Max is updated in a rolling manner. Compared to previous versions, it shows significant improvements in both Chinese and English code generation, logical reasoning, and multilingual abilities. The response style has been greatly adjusted to align with human preferences, with noticeable enhancements in the level of detail and clarity of responses. Specialized improvements have been made in creative writing, adherence to JSON formatting, and role-playing abilities.

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

Web Search

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

feature.funeTuning

Pricing

  • Input
    $1.6Per 1M tokens
  • Output
    $6.4Per 1M tokens
  • Input(Implicit Cache)
    $0.32Per 1M tokens
  • Input(Batch File)
    $0.8Per 1M tokens
  • Output(Batch File)
    $3.2Per 1M tokens

toc.rateLimitsAndContext

  • Max Input
    30.72K
  • Max Output
    8.19K
  • RPMRequests Per Minute
    600
  • TPMTokens Per Minute
    1M
  • Context
    32.76K

API Reference

Get API Key
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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="qwen-max",
    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)