Qwen 3.6 35B-A3B vs Qwen 3 30B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3.6 35B-A3B | Qwen 3 30B-A3B |
|---|---|---|
| Parameters | 35B | 30B |
| Author | Alibaba | Alibaba |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 21 GB | 19 GB |
| VRAM at Q5 | 25 GB | 23 GB |
| VRAM at Q8 | 38 GB | 35 GB |
| VRAM at FP16 | 70 GB | 62 GB |
| Use cases | chat, code, reasoning, moe | chat, general, reasoning, multilingual, moe |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Qwen 3.6 35B-A3B
Alibaba's agentic coding MoE with 35B total and just 3B active parameters, released April 16, 2026. Scores 73.4% on SWE-Bench while running on a single 24GB GPU. Strengths: 73.4% SWE-Bench in an MoE that fits on a 24GB GPU, Only 3B active parameters means fast inference, 262K context handles whole repos, Apache 2.0 license.
About Qwen 3 30B-A3B
Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0. Strengths: 3B active parameters keeps inference fast and cheap, MMLU 81.4 and AIME24 80.4 — strong on both general and reasoning, Apache 2.0, Hybrid thinking toggle per request.
How they compare
Qwen 3.6 35B-A3B comes from Alibaba and Qwen 3 30B-A3B from Alibaba. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 35B vs 30B parameters, Qwen 3.6 35B-A3B is the larger of the two. At Q4, Qwen 3 30B-A3B fits in about 19 GB of VRAM versus 21 GB for the other — a 2 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Qwen 3.6 35B-A3B is tuned for chat, code, reasoning, moe, while Qwen 3 30B-A3B leans toward chat, general, reasoning, multilingual, moe. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Qwen 3 30B-A3B pushes roughly 40 tokens/sec versus 22, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3.6 35B-A3B offers the bigger window (255k vs 128k tokens).
Memory, quantization & throughput
Across quantization levels, Qwen 3.6 35B-A3B requires Q4 ≈ 21 GB, Q5 ≈ 25 GB, Q8 ≈ 38 GB, FP16 ≈ 70 GB, while Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB. In practice Qwen 3.6 35B-A3B wants a 24 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Qwen 3.6 35B-A3B needs roughly 28 GB of system RAM to run on CPU and Qwen 3 30B-A3B about 32 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 22 tokens/sec from Qwen 3.6 35B-A3B and 40 from Qwen 3 30B-A3B, scaling up to 60 and 100 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Qwen 3.6 35B-A3B or Qwen 3 30B-A3B to the card you actually own:
- On a 24 GB GPU: Qwen 3.6 35B-A3B runs at Q4 (21 GB); Qwen 3 30B-A3B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for Qwen 3.6 35B-A3B: SWE-Bench 73.4.
Reported benchmarks for Qwen 3 30B-A3B: MMLU (base) 81.38, AIME 2024 80.4.
Bottom line: which should you pick?
- Pick Qwen 3.6 35B-A3B for long-context work (up to 255k tokens).
- Pick Qwen 3 30B-A3B for lower VRAM and faster inference; pick Qwen 3.6 35B-A3B for maximum headline quality.
- Pick Qwen 3.6 35B-A3B if your workload is code.
- Pick Qwen 3 30B-A3B if your workload is general, multilingual.
Which GPU should you buy to run Qwen 3.6 35B-A3B?
To run Qwen 3.6 35B-A3B locally at Q4, you need ~21 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between Qwen 3.6 35B-A3B and Qwen 3 30B-A3B?
The headline differences: Qwen 3.6 35B-A3B is a 35B model and Qwen 3 30B-A3B is 30B; their context windows differ (255k vs 128k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Qwen 3.6 35B-A3B and Qwen 3 30B-A3B run on a 24 GB GPU?
At a Q4 quantization, Qwen 3.6 35B-A3B needs about 21 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 3 30B-A3B needs about 19 GB and fits comfortably on a 24 GB GPU. Qwen 3 30B-A3B is the lighter option for tight VRAM budgets.
Which is faster, Qwen 3.6 35B-A3B or Qwen 3 30B-A3B?
Qwen 3 30B-A3B is the smaller model (30B vs 35B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Qwen 3.6 35B-A3B and Qwen 3 30B-A3B use?
Qwen 3.6 35B-A3B is licensed under Apache 2.0 and Qwen 3 30B-A3B under Apache 2.0.
Which has the longer context window, Qwen 3.6 35B-A3B or Qwen 3 30B-A3B?
Qwen 3.6 35B-A3B has the larger context window (255k vs 128k tokens), so it handles longer documents and codebases in a single prompt.