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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.

定價

按用量付費

無需預付費用,僅按實際使用量付費

輸入
128K $1.30 / M Tokens
> 128K $2.00 / M Tokens
輸出
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

試用模型

qwen/qwen3.6-max-preview
線上
alibaba
嗨!我是樂於助人的 AI 助理。有什麼可以幫你的嗎?

API 使用

使用以下程式碼範例整合我們的 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)

模型介紹

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

資訊

提供商alibaba
類型llm

支援功能

輸入
文字
輸出
文字
上下文262,144
最大輸出65,536
視覺-
函式呼叫✓ 支援

API 存取指南

Base URLhttps://llm.wavespeed.ai/v1
API 端點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.

輸入

$1.3 /M

輸出

$7.8 /M

上下文

262K

最大輸出

66K

工具調用

支援

在 WaveSpeedAI 試用 Qwen3.6 Max Preview

透過我們的統一 API 接入 Qwen3.6 Max Preview — 相容 OpenAI、無冷啟動、透明計費。

關於 Qwen3.6 Max Preview 的常見問題

Qwen3.6 Max Preview API 多少錢?+

WaveSpeedAI 定價:輸入每百萬 token $1.30,輸出每百萬 token $7.80。Prompt 快取與批次處理分別計費,可顯著降低長上下文、高重複任務的實際成本。

Qwen3.6 Max Preview 的上下文視窗有多大?+

Qwen3.6 Max Preview 每次請求最多支援 262K 上下文 token,輸出最多 66K token。

Qwen3.6 Max Preview 是否相容 OpenAI?+

是的。WaveSpeedAI 透過 https://llm.wavespeed.ai/v1 的 OpenAI 相容端點提供 Qwen3.6 Max Preview。將官方 OpenAI SDK 的 base URL 指向該位址,使用 WaveSpeedAI 的 API Key 即可,無需其他程式碼變更。

如何開始使用 Qwen3.6 Max Preview?+

登入 WaveSpeedAI,在 Access Keys 建立 API Key,使用上方顯示的 model id 向 https://llm.wavespeed.ai/v1/chat/completions 發送請求。新帳號將獲得免費額度,用於試用 Qwen3.6 Max Preview。

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