Structured Output
Generate JSON, XML, and schema-compliant data reliably.
Qwen3.5-35B-A3B — Compact MoE for Deep Reasoning
Qwen3.5-35B-A3B is a compact MoE model that activates only 3B parameters per step — ideal for reasoning-heavy tasks, structured output, and tool use. Try it free.
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Qwen3.5-35B-A3B is the default model for this page. MoE option for reasoning-heavy chats, structured work, and deeper tool use.
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MoE
Reasoning
Starter prompts
Free to try in the browser. Qwen3.5-Flash uses this model as its public reference base.
Total Params
35B
Active Params
3B
Context
262K native
License
Apache 2.0
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Overview
Why Mixture-of-Experts Matters
Qwen3.5-35B-A3B is the smallest MoE model in the Qwen 3.5 family. MoE architecture routes each token to a small subset of expert networks, keeping inference fast while drawing on a much larger knowledge base. Compared to Qwen3.5-27B (dense, 27B active), this model activates only 3B parameters per token yet often matches or exceeds 27B on reasoning benchmarks.
Expert Routing
Only 3B parameters active per token — fast inference despite 35B total size.
Reasoning-Heavy
Excels at structured output, multi-step logic, and tool-use scenarios.
Cost Efficient
Lower compute cost per token than equivalently capable dense models.
Qwen3.5-35B-A3B Benchmark
How Qwen3.5-35B-A3B compares to nearby models in the Qwen family.
Model
MMLU-Pro
GPQA / GPQA-family
LiveCodeBench v6
Qwen3.5-27B
Balanced dense model with better reasoning and coding depth.
Updated 2026-04-02
MMLU-Pro
86.1
GPQA / GPQA-family
85.5
LiveCodeBench v6
80.7
Qwen3.5-35B-A3B
Compact MoE model, also the base model behind Qwen3.5-Flash.
Updated 2026-04-02
MMLU-Pro
85.3
GPQA / GPQA-family
84.2
LiveCodeBench v6
74.6
Qwen3.5-Flash
HostedHosted version built on Qwen3.5-35B-A3B with additional tooling and a 1M context window.
Scores reference the Qwen3.5-35B-A3B base model.
Updated 2026-04-02
MMLU-Pro
85.3
GPQA / GPQA-family
84.2
LiveCodeBench v6
74.6
Scores are from public model cards and the qwen.ai release page. Hosted models are labeled with their open-weight base.
Updated 2026-04-02Use Cases
What Qwen3.5-35B-A3B Is Best For
Ideal when you need more reasoning depth than dense models provide, without the resource demands of the larger MoE variants.
Multi-Step Reasoning
Chain-of-thought tasks, math problems, and logical deduction.
Tool Use & Agents
Function calling, API orchestration, and agentic workflows.
Code Analysis
Understand complex codebases and generate structured refactoring plans.
Research Summarization
Condense technical papers and reports into actionable insights.
Efficient Deployment
Deploy locally with less VRAM than larger MoE or dense models.
FAQ
Qwen3.5-35B-A3B FAQ
Common questions about the compact MoE model.
What does 35B-A3B mean?
35B is the total parameter count across all experts. A3B means only 3 billion parameters are activated per token during inference, making it much faster than a full 35B dense model.
How does it compare to Qwen3.5-27B?
27B is fully dense — every parameter is used for every token. 35B-A3B routes to experts, so it uses fewer active parameters but draws on a wider knowledge base. It often matches 27B on reasoning while being faster.
Can I run it locally?
Yes, but the practical footprint depends on precision, framework, and context length. The model card focuses on public serving guides, and community quantized formats may reduce the footprint further.
When should I use the larger MoE models instead?
If 35B-A3B struggles with very complex multi-step tasks or long-form generation, step up to Qwen3.5-122B-A10B or Qwen3.5-397B-A17B for more expert capacity.
How much VRAM does the 35B-A3B model need?
Despite 35B total parameters, only 3B are active per token. Quantized versions can run on 8-12 GB VRAM depending on the framework.
Is 35B-A3B the same as Qwen3.5-Flash?
Qwen3.5-Flash is the hosted version built on 35B-A3B. Flash adds production tooling and a 1M context window. The 35B-A3B weights are what you can download and self-host.
What tasks is 35B-A3B best for?
Reasoning-heavy conversations, structured output, and tasks where you want MoE-level reasoning without the hardware cost of the larger models.
What context window does 35B-A3B support?
Qwen3.5-35B-A3B supports 262,144 native tokens and can be extended higher with the right serving stack.