What are the strengths of this model?

Advanced reasoning capabilities 1M long-context processing Large scale 350B parameters

What are the weaknesses of this model?

Closed license High computational resource requirements Commercial use restrictions

What are the best use cases?

Complex logical reasoning Ultra-long document analysis Advanced knowledge extraction

Back to Models

AlibabaProprietary

Qwen3.5-35B-A3B

Name: Qwen3.5-35B-A3B
Author: Alibaba

Qwen3.5-35B-A3B is a reasoning model developed by Alibaba. It boasts a parameter scale of approximately 350B and supports an extensive context window of up to 1M.

Parameters

350.0B

Context Window

License

https://huggingface.co/Qwen/Qwen2.5-72B/blob/main/LICENSE

Release Date

2026-02-25

API Pricing

API pricing for this model is not yet available

Strengths

・Advanced reasoning capabilities
・1M long-context processing
・Large scale 350B parameters

Weaknesses

・Closed license
・High computational resource requirements
・Commercial use restrictions

Use Cases

・Complex logical reasoning
・Ultra-long document analysis
・Advanced knowledge extraction

Deep Analysis

Release Date

February 2026

Total Parameters

35B

MoE with 256 experts

Active Parameters

3B per token

Only 3B active — ultra-efficient

Context Window

262,144 tokens

Architecture

Hybrid MoE: Gated DeltaNet + Gated Attention

Modalities

Text, Image, Video

Inference Speed

196 tok/s on RTX 4090

111 tok/s on RTX 3090 at Q4

VRAM (Q4)

~22 GB

License

Apache 2.0

AA Intelligence Index

More than double the class median of 15

Strengths

・Incredible speed: 196 tok/s on RTX 4090 with only 3B active parameters per token
・Beats previous-gen Qwen3-235B-A22B on core benchmarks despite being much smaller
・Fits on a single RTX 3090/4090 at Q4 quantization (~22GB VRAM)
・Community favorite: r/LocalLLaMA calls it 'the model that's all you need' for practical tasks
・Natively multimodal with text, image, and video support

Weaknesses

・Only 3B active parameters limits performance on the most complex reasoning tasks
・Creative writing quality may be inferior to the denser 27B model
・LiveCodeBench performance lags behind larger models
・MoE architecture still requires full 35B parameter weights in memory
・Successor Qwen3.6-35B-A3B already announced, making this slightly dated

Competitor Comparison

Model	Arena	SWE	GPQA	Price
Qwen3.5-27B	~1400	~68	85.5	Open-source
Qwen3.5-9B	~1370	~60	81.7	Open-source
Llama 4 Scout	~1380	~65	~80	Open-source
Qwen3.5-35B-A3B	~1390	~65	~83	Open-source
Mistral Large	~1380	~64	~78	Open-source

Overview

Qwen3.5-35B-A3B is the speed champion of the Qwen3.5 family — a 35B MoE model that activates only 3B parameters per token, achieving 196 tok/s on an RTX 4090 at Q4 quantization. Despite its minimal active compute, it beats the previous-generation 235B-A22B model on core benchmarks. It is the community's recommended daily-driver model for local AI, fitting comfortably on a single consumer GPU.

Benchmarks & Performance

The 35B-A3B punches far above its weight class. The Artificial Analysis Intelligence Index rates it at 37 — more than double the median score of 15 for its class. On MMLU-Pro it scores in the ~82 range, GPQA Diamond ~83, with strong instruction following. The speed advantage is the headline: 196 tok/s on RTX 4090, 111 tok/s on RTX 3090 at Q4. On M4 Max via MLX, it hits 60-70 tok/s. These speeds make it viable for real-time interactive applications.

Detailed Comparison

Compared to the 27B dense model: significantly faster (196 vs 35 tok/s on 4090) but slightly lower quality on creative writing and complex reasoning. Compared to the 9B: more capable on reasoning tasks with only marginally more VRAM. Compared to Llama 4 Scout and Mistral Large: competitive quality with dramatically better inference speed. The 3B active parameter design means inference cost is comparable to a 3B dense model.

Community Feedback

Enthusiastic reception on r/LocalLLaMA and local AI communities. Widely recommended as the best model for consumer GPU deployment. Users praise the speed-to-quality ratio. The 'all you need' moniker reflects genuine satisfaction with real-world performance. Some users prefer the 27B for writing-heavy tasks. The announcement of Qwen3.6-35B-A3B successor has not diminished enthusiasm for the 3.5 version.

Use Cases

The ideal model for local AI enthusiasts and developers with 24GB GPUs. Excellent for coding assistance, batch processing, agent workflows, chat, summarization, and document analysis. The high speed makes it suitable for real-time applications and interactive development. For creative writing, the 27B dense model may be preferable. For teams, API access through DashScope and various providers eliminates hardware concerns.

Latest News

Released February 2026. Widely available on HuggingFace with GGUF quantizations from Unsloth. API access via DashScope, SiliconFlow, Artificial Analysis, and others. Qwen3.6-35B-A3B successor announced in April 2026.

Sources

Analysis generated: 2026-05-24