Qwen3-Max-2026-01-23

Qwen3-Max

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Text GenerationReasoning

Overview

Text GenerationReasoning

Compared with the snapshot as of September 23, 2025, the Qwen-3 series Max model in this release achieves an effective integration of thinking and non-thinking modes, resulting in a comprehensive and substantial improvement in the model’s overall performance. In thinking mode, the model simultaneously supports web search, web information extraction, and a code interpreter tool, enabling it to tackle more complex and challenging problems with greater accuracy by leveraging external tools while engaging in slow, deliberative reasoning. This version is based on a snapshot taken on January 23, 2026.

Input

Text

Output

Text

Features

Prefix Completion

Function Calling

Cache

Structured Outputs

Batches

Web Search

Pricing

Input
$1.2Per 1M tokens
Output
$6Per 1M tokens

Input
$1.2Per 1M tokens
Output
$6Per 1M tokens

Context

262.14K

Max Input

258.04K

Max Output

65.53K

Rate Limits

RPMRequests Per Minute
600
TPMTokens Per Minute
1M

Built-in Tools

search_strategy:agentCompletions API

search_strategy:agent_maxCompletions API

web_searchResponses API

code_interpreterResponses API

web_extractorResponses API

API Reference

Get API Key

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import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response = Generation.call(
    # 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"),
    model="qwen3-max-2026-01-23",
    messages=messages,
    result_format="message",
    # Enable deep thinking
    enable_thinking=True,
)

if response.status_code == 200:
    # Print thinking process
    print("=" * 20 + "Thinking process" + "=" * 20)
    print(response.output.choices[0].message.reasoning_content)
    
    # Print response
    print("=" * 20 + "Full response" + "=" * 20)
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP return code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")

import os
from dashscope import Generation
import dashscope
dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"},
]
response = Generation.call(
    # 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"),
    model="qwen3-max-2026-01-23",
    messages=messages,
    result_format="message",
    # Enable deep thinking
    enable_thinking=True,
)

if response.status_code == 200:
    # Print thinking process
    print("=" * 20 + "Thinking process" + "=" * 20)
    print(response.output.choices[0].message.reasoning_content)
    
    # Print response
    print("=" * 20 + "Full response" + "=" * 20)
    print(response.output.choices[0].message.content)
else:
    print(f"HTTP return code: {response.status_code}")
    print(f"Error code: {response.code}")
    print(f"Error message: {response.message}")