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alibaba
qwen/qwen3.6-max-preview

qwen/qwen3.6-max-preview

262,144 context · $1.30/M input tokens · $7.80/M output tokens

Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts architecture with approximately 1T total parameters. It is optimized for high-end reasoning, agentic coding, tool use, instruction following, and complex text generation workflows. The model supports a 262K-token context window, up to 64K output tokens, thinking mode, function calling, and structured outputs, making it suitable for demanding production tasks that require stronger reasoning capability over raw throughput.

定价

按量付费

无需预付费用,仅按实际使用量付费

输入
128K $1.30 / M Tokens
> 128K $2.00 / M Tokens
输出
128K $7.80 / M Tokens
> 128K $12.00 / M Tokens
Cache Read
128K $0.13 / M Tokens
> 128K $0.20 / M Tokens
Cache Write$1.63 / M Tokens

试用模型

qwen/qwen3.6-max-preview
在线
alibaba
你好!我是乐于助人的 AI 助手。需要我帮你做什么?

API 使用

使用以下代码示例接入我们的 API:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.6-max-preview",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

模型介绍

Qwen: Qwen3.6 Max Preview

Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts architecture with approximately 1T total parameters. It is optimized for high-end reasoning, agentic coding, tool use, instruction following, and complex text generation workflows.


Why It Looks Great

  • Proprietary frontier Qwen model for demanding reasoning and coding workloads
  • Sparse Mixture-of-Experts architecture with approximately 1T total parameters
  • Strong fit for agentic coding, tool use, complex reasoning, and instruction-following tasks
  • 262K-token context window for long prompts, repository-scale context, documents, and multi-turn workflows
  • Up to 64K output tokens for extended reasoning, coding, and structured generation
  • Thinking mode support for reasoning-heavy requests
  • Function calling and tool-use support for agentic application workflows
  • Structured output support for JSON responses and schema-constrained generation

Key Features

  • Architecture: Sparse Mixture-of-Experts
  • Total Parameters: approximately 1T
  • Context Window: 262,144 tokens
  • Max Input: 196,608 tokens
  • Max Output: 65,536 tokens
  • Thinking Budget: 128K tokens
  • Input: Text
  • Output: Text
  • Vision: Not listed
  • Function Calling: Supported
  • Structured Outputs: Supported
  • Thinking Mode: Supported
  • Built-in Tools: Not listed
  • Batch Calling: Not listed
  • Audio Input: Not listed
  • Image Generation: Not listed
  • Supported Parameters: include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p

Specifications

SpecificationValue
Provideralibaba
Model TypeChat Completions model
ArchitectureSparse Mixture-of-Experts
Parametersapproximately 1T
Modalitiestext->text
Context Window262,144 tokens
Max Input196,608 tokens
Max Output65,536 tokens
Thinking Budget128K tokens
InputText
OutputText
VisionNot listed
Function CallingSupported
Structured OutputsSupported
Thinking ModeSupported
ReleaseApril 2026

Pricing

Token TypeCost
Input$1.04 per million tokens
Output$6.24 per million tokens
Cache Write$1.30 per million tokens

How to Use

  1. Write your prompt - describe the task, provide context, and specify the desired output format.
  2. Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1
API Endpoint: chat/completions
Model ID: qwen/qwen3.6-max-preview


API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.6-max-preview",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "qwen/qwen3.6-max-preview",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

  • Model: qwen/qwen3.6-max-preview
  • Provider: alibaba
  • Best suited for high-end reasoning, agentic coding, tool use, long-context text workflows, and structured output generation

信息

提供商alibaba
类型llm

支持功能

输入
文本
输出
文本
上下文262,144
最大输出65,536
视觉-
函数调用✓ 支持

API 访问指南

Base URLhttps://llm.wavespeed.ai/v1
API 端点chat/completions
Model IDqwen/qwen3.6-max-preview

Qwen3.6 Max Preview API

qwen/qwen3.6-max-preview

Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse Mixture-of-Experts architecture with approximately 1T total parameters. It is optimized for high-end reasoning, agentic coding, tool use, instruction following, and complex text generation workflows. The model supports a 262K-token context window, up to 64K output tokens, thinking mode, function calling, and structured outputs, making it suitable for demanding production tasks that require stronger reasoning capability over raw throughput.

输入

$1.3 /M

输出

$7.8 /M

上下文

262K

最大输出

66K

工具调用

支持

在 WaveSpeedAI 试用 Qwen3.6 Max Preview

通过我们的统一 API 接入 Qwen3.6 Max Preview — 兼容 OpenAI、无冷启动、透明计费。

关于 Qwen3.6 Max Preview 的常见问题

Qwen3.6 Max Preview API 多少钱?+

WaveSpeedAI 定价:输入每百万 token $1.30,输出每百万 token $7.80。Prompt 缓存和批处理单独计费,可显著降低长上下文、高重复任务的实际成本。

Qwen3.6 Max Preview 的上下文窗口是多大?+

Qwen3.6 Max Preview 单次请求最多支持 262K 上下文 token,输出最多 66K token。

Qwen3.6 Max Preview 是否兼容 OpenAI?+

是的。WaveSpeedAI 通过 https://llm.wavespeed.ai/v1 的 OpenAI 兼容端点提供 Qwen3.6 Max Preview。把官方 OpenAI SDK 的 base URL 指向该地址,使用 WaveSpeedAI 的 API Key 即可,无需任何其他代码改动。

如何开始使用 Qwen3.6 Max Preview?+

登录 WaveSpeedAI,在 Access Keys 中生成 API Key,使用上方显示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 发送请求。新账户可获得免费额度,用于试用 Qwen3.6 Max Preview。

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