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

Function Calling

Cache

Structured Outputs

Batches

Web Search

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

Context

32.76K

Max Input

30.72K

Max Output

8.19K

Rate Limits

RPMRequests Per Minute
600
TPMTokens Per Minute
1M

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://[workspace-id].ap-southeast-1.maas.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)

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://[workspace-id].ap-southeast-1.maas.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)