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alibaba
qwen/qwen3.6-35b-a3b

qwen/qwen3.6-35b-a3b

262,144 context · $0.25/M input tokens · $2.00/M output tokens

Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35B total parameters and 3B active parameters per token. It uses a hybrid sparse Mixture-of-Experts architecture that combines Gated DeltaNet linear attention with standard attention layers, giving it strong efficiency for coding, agentic workflows, long-context reasoning, and multimodal understanding. The model supports text, image, and video inputs, a 262K-token context window, thinking and non-thinking modes, function calling, and structured outputs.

定價

按用量付費

無需預付費用,僅按實際使用量付費

輸入$0.25 / M Tokens
輸出$2.00 / M Tokens

試用模型

qwen/qwen3.6-35b-a3b
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/qwen3.6-35b-a3b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

模型介紹

Qwen: Qwen3.6 35B A3B

Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35B total parameters and 3B active parameters per token. It uses a hybrid sparse Mixture-of-Experts architecture combining Gated DeltaNet linear attention with standard attention layers, giving it strong efficiency for coding, agentic workflows, long-context reasoning, and multimodal understanding.


Why It Looks Great

  • Open-weight 35B sparse MoE model with only 3B active parameters per token
  • Hybrid architecture combining Gated DeltaNet linear attention, standard attention, and MoE feed-forward layers
  • Native multimodal support for text, image, and video understanding
  • 262K-token context window for long prompts, large documents, codebases, and multi-turn workflows
  • Efficient active-parameter design for cost-sensitive production inference
  • Supports both thinking and non-thinking modes for flexible latency and reasoning trade-offs
  • Function calling and tool-use support for agentic application workflows
  • Structured output support for JSON responses and schema-constrained generation

Key Features

  • Total Parameters: 35B
  • Active Parameters: 3B per token
  • Architecture: Sparse Mixture-of-Experts
  • Context Window: 262,144 tokens
  • Max Input: Not listed
  • Max Output: Not listed
  • Input: Text, Image, Video
  • Output: Text
  • Vision: Supported
  • Function Calling: Supported
  • Structured Outputs: Supported
  • Thinking Mode: Supported
  • Audio Input: Not listed
  • Image Generation: Not listed
  • Supported Parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

Specifications

SpecificationValue
Provideralibaba
Model TypeChat Completions model
ArchitectureSparse MoE, 35B total / 3B active
AttentionGated DeltaNet + standard attention
Modalitiestext+image+video->text
Context Window262,144 tokens
Max InputNot listed
Max OutputNot listed
InputText, Image, Video
OutputText
VisionSupported
Function CallingSupported
Structured OutputsSupported
Thinking ModeSupported
ReleaseApril 2026

Pricing

Token TypeCost
Input$0.149 per million tokens
Output$1.00 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/qwen3.6-35b-a3b


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/qwen3.6-35b-a3b",
    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/qwen3.6-35b-a3b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

  • Model: qwen/qwen3.6-35b-a3b
  • Provider: alibaba
  • Best suited for efficient multimodal reasoning, coding, agentic workflows, long-context document understanding, and structured extraction

資訊

提供商alibaba
類型llm

支援功能

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

API 存取指南

Base URLhttps://llm.wavespeed.ai/v1
API 端點chat/completions
Model IDqwen/qwen3.6-35b-a3b

Qwen3.6 35b A3b API

qwen/qwen3.6-35b-a3b

Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35B total parameters and 3B active parameters per token. It uses a hybrid sparse Mixture-of-Experts architecture that combines Gated DeltaNet linear attention with standard attention layers, giving it strong efficiency for coding, agentic workflows, long-context reasoning, and multimodal understanding. The model supports text, image, and video inputs, a 262K-token context window, thinking and non-thinking modes, function calling, and structured outputs.

輸入

$0.25 /M

輸出

$2 /M

上下文

262K

Vision

支援

工具調用

支援

在 WaveSpeedAI 試用 Qwen3.6 35b A3b

透過我們的統一 API 接入 Qwen3.6 35b A3b — 相容 OpenAI、無冷啟動、透明計費。

關於 Qwen3.6 35b A3b 的常見問題

Qwen3.6 35b A3b API 多少錢?+

WaveSpeedAI 定價:輸入每百萬 token $0.25,輸出每百萬 token $2.00。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 35b A3b 的上下文視窗有多大?+

Qwen3.6 35b A3b 每次請求最多支援 262K 上下文 token,輸出最多 — token。

Qwen3.6 35b A3b 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 35b A3b。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 35b A3b?+

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 35b A3b。

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