Qwen3.5 35B-A3B

qwen3.5-35b-a3b

Qwen3.5 35B A3B is a native vision-language MoE model designed to approximate much larger model behavior with a smaller active footprint. Model cards describe hybrid linear attention, sparse experts, and comparable results to larger Qwen3.5 dense variants. The strongest positioning is efficient multimodal reasoning and coding without the cost of always activating a large dense model.

Context Window

262.1K tokens

Maximum Output

65.5K tokens

Release Date

Feb 23, 2026

Modalities

Qwen3.5 35B-A3B Pricing

Token Tier	Input Price	Output Price
<=128K	$0.0571/M	$0.4571/M
>128K	$0.2286/M	$1.8286/M

Qwen3.5 35B-A3B API Capabilities

Reasoning

Supported

Tool calling

Supported

Temperature parameter

Supported

Attachments

Supported

Knowledge Base

—

Endpoint Protocols

geminiCompletions APIMessages API

Qwen3.5-35B-A3B Model Highlights

Qwen3.5-35B-A3B combines an efficient sparse MoE design, native multimodal understanding and open-weight deployment for adaptable applications.

Sparse MoE Efficiency

The model has 35 billion total parameters with 3 billion activated, combining Gated DeltaNet attention with sparse experts for efficient inference.

Native Multimodality

A unified vision-language foundation processes text, images and video for reasoning, document understanding and visual tasks.

Open-Weight Deployment

Official weights under Apache 2.0 support self-managed inference with frameworks including Transformers, vLLM and SGLang.

Qwen3.5-35B-A3B Use Cases

Qwen3.5-35B-A3B is suited to self-hosted multimodal assistants, visual document processing and adaptable coding workflows.

Private Multimodal Assistant

Deploy the open weights in controlled infrastructure to answer questions over internal text, images and videos.

Visual Document Processing

Read text, tables and diagrams from document images, extract relevant fields and generate structured summaries.

Coding Workflows

Generate and revise code, investigate defects and integrate the model into self-managed developer tools or agents.

How to Use Qwen3.5 35B-A3B via the TokenHub API

Create API key

Replace these path values before running: {model}

curl 'https://us-api.tokenhub.com/v1beta/models/{model}:generateContent' \
  -X 'POST' \
  -H "Authorization: Bearer $TOKENHUB_API_KEY"

Qwen3.5 35B-A3B Benchmarks

Index score
Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index summarizes broad model capability across evaluation families.	Artificial Analysis broad capability aggregate	23.4
Artificial Analysis Coding IndexArtificial Analysis Coding Index summarizes model performance on coding and software engineering tasks.	Artificial Analysis software task aggregate	16.8
Knowledge & Reasoning
GPQAGPQA is a graduate-level science benchmark designed to test expert reasoning.	Advanced science problem solving	81.9%
HLEHLE is a broad, challenging benchmark for expert-level knowledge and reasoning across many domains.	Broad expert-level exam set	12.8%
Coding & Engineering
SciCodeSciCode evaluates Python programming for scientific computing, including algorithmic implementation from scientific context.	Scientific coding challenges	29.3%
Terminal-Bench HardTerminal-Bench Hard evaluates whether an AI agent can complete difficult tasks in a terminal environment.	Hard terminal task execution	10.6%
Instruction Following & Agent Tasks
IFBenchIFBench measures how reliably a model follows detailed instructions and constraints.	Prompt constraint adherence	44.5%
AA-LCRAA-LCR measures reasoning quality over extended context windows.	Long-context reasoning	55.3%
τ²-Benchτ²-Bench evaluates conversational agents in realistic workflows with tool and state constraints.	Agent workflow tasks	86.3%

Metrics sourced from Artificial Analysis

Qwen 3.5 35B A3B FAQ

Qwen 3.5 35B A3B: capabilities, use cases, limits, and TokenHub guidance.

How should teams view Qwen 3.5 35B A3B?+

Qwen 3.5 35B A3B is a Alibaba Qwen model for open-model multimodal reasoning and efficient deployment.

What is Qwen 3.5 35B A3B best for?+

Best for self-hosted deployment, visual reasoning and routine coding assistance, especially when deployment control is the priority.

What is Qwen 3.5 35B A3B's main strength?+

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

Is Qwen 3.5 35B A3B always the best choice?+

It belongs to an older generation and may lack newer capabilities. For the latest capabilities matter, consider Qwen 3.6 35B A3B.

What is the safest setup?+

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

Ready to use Qwen3.5 35B-A3B?

Use one API key to access Qwen3.5 35B-A3B and more AI models through TokenHub.

Create API key

Qwen3.5 35B-A3B

Qwen3.5 35B-A3B Pricing

Qwen3.5 35B-A3B API Capabilities

Qwen3.5-35B-A3B Model Highlights

Sparse MoE Efficiency

Native Multimodality

Open-Weight Deployment

Qwen3.5-35B-A3B Use Cases

Private Multimodal Assistant

Visual Document Processing

Coding Workflows

How to Use Qwen3.5 35B-A3B via the TokenHub API

Qwen3.5 35B-A3B Benchmarks

Models Similar to Qwen3.5 35B-A3B

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Qwen 3.5 35B A3B FAQ

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