Qwen3.6 35B-A3B

qwen3.6-35b-a3b

Qwen3.6 35B A3B is a compact active-parameter MoE model: sources describe 35B total parameters with around 3B activated parameters, plus native long context that can be extended toward 1M tokens. Official materials compare it favorably with larger dense models on coding tasks despite the small active footprint. Its value proposition is efficient deployment with surprisingly strong coding and long-context ability.

Context Window

262.1K tokens

Maximum Output

65.5K tokens

Release Date

Apr 17, 2026

Modalities

Qwen3.6 35B-A3B Pricing

Input Price	Output Price
$0.2571/M	$1.5429/M

Qwen3.6 35B-A3B API Capabilities

Reasoning

Supported

Tool calling

Supported

Temperature parameter

Supported

Attachments

Supported

Knowledge Base

—

Endpoint Protocols

Completions APIMessages APIgemini

Qwen3.6-35B-A3B Model Highlights

Qwen3.6-35B-A3B combines an open-weight sparse MoE architecture with native vision-language processing and improved agentic coding.

Sparse MoE Efficiency

Its hybrid linear-attention and sparse mixture-of-experts design activates about 3B of 35B parameters to improve inference efficiency.

Native Multimodality

A native vision-language design accepts text, images and video and produces text grounded in visual content.

Open-Weight Agentic Coding

The post-trained open-weight model improves frontend workflows and repository-level reasoning while allowing independent deployment.

Qwen3.6-35B-A3B Use Cases

Qwen3.6-35B-A3B supports independently deployed coding agents, repository analysis and visual reasoning over images or video.

Self-Hosted Coding Agent

Deploy the open weights in a controlled environment to generate, revise and debug code through iterative agent workflows.

Repository Code Analysis

Trace relationships across repository files, explain implementation structure and prepare coordinated modification plans.

Visual Media Analysis

Analyze images or video to locate objects, interpret spatial relationships and return a structured textual account.

How to Use Qwen3.6 35B-A3B via the TokenHub API

Create API key

import OpenAI from "openai"

const client = new OpenAI({
  apiKey: process.env.TOKENHUB_API_KEY,
  baseURL: "https://us-api.tokenhub.com/v1",
})

const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)

Qwen3.6 35B-A3B Benchmarks

Index score
Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index summarizes broad model capability across evaluation families.	Artificial Analysis broad capability aggregate	33
Artificial Analysis Coding IndexArtificial Analysis Coding Index summarizes model performance on coding and software engineering tasks.	Artificial Analysis software task aggregate	35.2
Knowledge & Reasoning
GPQAGPQA is a graduate-level science benchmark designed to test expert reasoning.	Advanced science problem solving	84.1%
HLEHLE is a broad, challenging benchmark for expert-level knowledge and reasoning across many domains.	Broad expert-level exam set	20.2%
Coding & Engineering
SciCodeSciCode evaluates Python programming for scientific computing, including algorithmic implementation from scientific context.	Scientific coding challenges	35.8%
Terminal-Bench HardTerminal-Bench Hard evaluates whether an AI agent can complete difficult tasks in a terminal environment.	Hard terminal task execution	34.8%
Instruction Following & Agent Tasks
IFBenchIFBench measures how reliably a model follows detailed instructions and constraints.	Prompt constraint adherence	64.4%
AA-LCRAA-LCR measures reasoning quality over extended context windows.	Long-context reasoning	63.7%
τ²-Benchτ²-Bench evaluates conversational agents in realistic workflows with tool and state constraints.	Agent workflow tasks	95.3%

Metrics sourced from Artificial Analysis

Qwen 3.6 35B A3B FAQ

Qwen 3.6 35B A3B: capabilities, use cases, limits, and TokenHub guidance.

What does Qwen 3.6 35B A3B focus on?+

Qwen 3.6 35B A3B is a Alibaba Qwen model for open-model multimodal reasoning and efficient deployment.

Which projects fit Qwen 3.6 35B A3B?+

Best for self-hosted deployment, image and video understanding and routine coding assistance, especially when deployment control is the priority.

What is special about Qwen 3.6 35B A3B?+

Key strength: an open MoE variant with a small active-parameter footprint and hybrid thinking that can switch between deliberate and direct responses.

When is another model better?+

Open deployment requires infrastructure, serving, and evaluation work. For stable production behavior matters, consider Qwen 3.6 Plus.

How do I avoid ID mistakes?+

Use TokenHub's exact ID; hosted behavior may differ from self-hosting.

Ready to use Qwen3.6 35B-A3B?

Use one API key to access Qwen3.6 35B-A3B and more AI models through TokenHub.

Create API key

Qwen3.6 35B-A3B

Qwen3.6 35B-A3B Pricing

Qwen3.6 35B-A3B API Capabilities

Qwen3.6-35B-A3B Model Highlights

Sparse MoE Efficiency

Native Multimodality

Open-Weight Agentic Coding

Qwen3.6-35B-A3B Use Cases

Self-Hosted Coding Agent

Repository Code Analysis

Visual Media Analysis

How to Use Qwen3.6 35B-A3B via the TokenHub API

Qwen3.6 35B-A3B Benchmarks

Models Similar to Qwen3.6 35B-A3B

Media and Discussions

X (Twitter)

Reddit

YouTube

Qwen 3.6 35B A3B FAQ

Ready to use Qwen3.6 35B-A3B?