Qwen 3 235B-A22B vs Llama 4 Maverick 400B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3 235B-A22B | Llama 4 Maverick 400B |
|---|---|---|
| Parameters | 235B | 400B |
| Author | Alibaba | Meta |
| License | Apache 2.0 | Llama 4 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 142 GB | 240 GB |
| VRAM at Q5 | 170 GB | 285 GB |
| VRAM at Q8 | 250 GB | 425 GB |
| VRAM at FP16 | 470 GB | 800 GB |
| Use cases | chat, general, reasoning, multilingual, moe | chat, general, vision, moe, multilingual |
Verdict
Llama 4 Maverick 400B is significantly larger (400B vs 235B), so expect higher quality but heavier VRAM and slower throughput.
For unambiguous commercial use, Qwen 3 235B-A22B has the safer license (Apache 2.0) compared to Llama 4 Community.
The two models at a glance
About Qwen 3 235B-A22B
Alibaba's flagship MoE — 235B total, 22B active per token across 128 experts. Hits 85.7 on AIME 2024 and 70.7 on LiveCodeBench, putting it in frontier-open territory. Strengths: Frontier-open scores on AIME 2024 (85.7) and LiveCodeBench (70.7), Only 22B active parameters — fast for its total size, Instruct-2507 and Thinking-2507 variants available, Apache 2.0.
About Llama 4 Maverick 400B
Meta's larger Llama 4 MoE at 400B total with 17B active across 128 experts, natively multimodal. LMArena 1417 and 1M token context, but 245GB to download. Strengths: LMArena 1417 — top-tier open chat quality, MMLU-Pro 80, 1M token context, Native multimodal with strong vision performance.
How they compare
Qwen 3 235B-A22B comes from Alibaba and Llama 4 Maverick 400B from Meta, they belong to the Qwen and Llama families respectively. 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 235B vs 400B parameters, Llama 4 Maverick 400B is the larger of the two. At Q4, Qwen 3 235B-A22B fits in about 142 GB of VRAM versus 240 GB for the other — a 98 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Qwen 3 235B-A22B is tuned for chat, general, reasoning, multilingual, moe, while Llama 4 Maverick 400B leans toward chat, general, vision, moe, multilingual. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Qwen 3 235B-A22B pushes roughly 12 tokens/sec versus 8, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 4 Maverick 400B offers the bigger window (976k vs 128k tokens).
Memory, quantization & throughput
Across quantization levels, Qwen 3 235B-A22B requires Q4 ≈ 142 GB, Q5 ≈ 170 GB, Q8 ≈ 250 GB, FP16 ≈ 470 GB, while Llama 4 Maverick 400B requires Q4 ≈ 240 GB, Q5 ≈ 285 GB, Q8 ≈ 425 GB, FP16 ≈ 800 GB. In practice Qwen 3 235B-A22B spills past 24 GB even 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 235B-A22B needs roughly 160 GB of system RAM to run on CPU and Llama 4 Maverick 400B about 280 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from Qwen 3 235B-A22B and 8 from Llama 4 Maverick 400B, scaling up to 28 and 22 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Qwen 3 235B-A22B: AIME 2024 85.7, AIME 2025 81.5, LiveCodeBench v5 70.7.
Reported benchmarks for Llama 4 Maverick 400B: LMArena 70.85, MMLU-Pro 80.
Bottom line: which should you pick?
- Pick Qwen 3 235B-A22B if you need a permissive (Apache 2.0) license for commercial deployment.
- Pick Llama 4 Maverick 400B for long-context work (up to 976k tokens).
- Pick Qwen 3 235B-A22B for lower VRAM and faster inference; pick Llama 4 Maverick 400B for maximum headline quality.
- Pick Qwen 3 235B-A22B if your workload is reasoning.
- Pick Llama 4 Maverick 400B if your workload is vision.
Which GPU should you buy to run Llama 4 Maverick 400B?
To run Llama 4 Maverick 400B locally at Q4, you need ~240 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Frequently asked questions
What is the difference between Qwen 3 235B-A22B and Llama 4 Maverick 400B?
The headline differences: Qwen 3 235B-A22B is a 235B model and Llama 4 Maverick 400B is 400B; their context windows differ (128k vs 976k tokens); they ship under different licenses (Apache 2.0 vs Llama 4 Community). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Qwen 3 235B-A22B and Llama 4 Maverick 400B run on a 24 GB GPU?
At a Q4 quantization, Qwen 3 235B-A22B needs about 142 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 4 Maverick 400B needs about 240 GB and needs more than 24 GB. Qwen 3 235B-A22B is the lighter option for tight VRAM budgets.
Which is faster, Qwen 3 235B-A22B or Llama 4 Maverick 400B?
Qwen 3 235B-A22B is the smaller model (235B vs 400B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
Which license is safer for commercial use, Qwen 3 235B-A22B or Llama 4 Maverick 400B?
Qwen 3 235B-A22B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 4 Community — check its terms before commercial deployment.
Which has the longer context window, Qwen 3 235B-A22B or Llama 4 Maverick 400B?
Llama 4 Maverick 400B has the larger context window (976k vs 128k tokens), so it handles longer documents and codebases in a single prompt.