Llama 4 Maverick 400B vs DeepSeek V3.2
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Llama 4 Maverick 400B | DeepSeek V3.2 |
|---|---|---|
| Parameters | 400B | 685B |
| Author | Meta | DeepSeek |
| License | Llama 4 Community | MIT |
| Context window | 0k | 0k |
| VRAM at Q4 | 240 GB | 410 GB |
| VRAM at Q5 | 285 GB | 490 GB |
| VRAM at Q8 | 425 GB | 735 GB |
| VRAM at FP16 | 800 GB | 1370 GB |
| Use cases | chat, general, vision, moe, multilingual | chat, general, moe |
Verdict
DeepSeek V3.2 is significantly larger (685B vs 400B), so expect higher quality but heavier VRAM and slower throughput.
For unambiguous commercial use, DeepSeek V3.2 has the safer license (MIT) compared to Llama 4 Community.
The two models at a glance
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.
About DeepSeek V3.2
DeepSeek's 685B MoE featuring DeepSeek Sparse Attention for lower memory use. Holds an IMO gold-medal score and ranks #2 by volume on OpenRouter. Strengths: IMO gold-medal reasoning quality, DeepSeek Sparse Attention reduces memory pressure, MIT license, #2 by usage volume on OpenRouter.
How they compare
Llama 4 Maverick 400B comes from Meta and DeepSeek V3.2 from DeepSeek, they belong to the Llama and DeepSeek 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 400B vs 685B parameters, DeepSeek V3.2 is the larger of the two. At Q4, Llama 4 Maverick 400B fits in about 240 GB of VRAM versus 410 GB for the other — a 170 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Llama 4 Maverick 400B is tuned for chat, general, vision, moe, multilingual, while DeepSeek V3.2 leans toward chat, general, moe. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Llama 4 Maverick 400B pushes roughly 8 tokens/sec versus 5, 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 125k tokens).
Memory, quantization & throughput
Across quantization levels, Llama 4 Maverick 400B requires Q4 ≈ 240 GB, Q5 ≈ 285 GB, Q8 ≈ 425 GB, FP16 ≈ 800 GB, while DeepSeek V3.2 requires Q4 ≈ 410 GB, Q5 ≈ 490 GB, Q8 ≈ 735 GB, FP16 ≈ 1370 GB. In practice Llama 4 Maverick 400B 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, Llama 4 Maverick 400B needs roughly 280 GB of system RAM to run on CPU and DeepSeek V3.2 about 512 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 8 tokens/sec from Llama 4 Maverick 400B and 5 from DeepSeek V3.2, scaling up to 22 and 15 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Llama 4 Maverick 400B: LMArena 70.85, MMLU-Pro 80.
Bottom line: which should you pick?
- Pick DeepSeek V3.2 if you need a permissive (MIT) license for commercial deployment.
- Pick Llama 4 Maverick 400B for long-context work (up to 976k tokens).
- Pick Llama 4 Maverick 400B for lower VRAM and faster inference; pick DeepSeek V3.2 for maximum headline quality.
- Pick Llama 4 Maverick 400B if your workload is multilingual, vision.
Which GPU should you buy to run DeepSeek V3.2?
To run DeepSeek V3.2 locally at Q4, you need ~410 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 Llama 4 Maverick 400B and DeepSeek V3.2?
The headline differences: Llama 4 Maverick 400B is a 400B model and DeepSeek V3.2 is 685B; their context windows differ (976k vs 125k tokens); they ship under different licenses (Llama 4 Community vs MIT). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Llama 4 Maverick 400B and DeepSeek V3.2 run on a 24 GB GPU?
At a Q4 quantization, Llama 4 Maverick 400B needs about 240 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); DeepSeek V3.2 needs about 410 GB and needs more than 24 GB. Llama 4 Maverick 400B is the lighter option for tight VRAM budgets.
Which is faster, Llama 4 Maverick 400B or DeepSeek V3.2?
Llama 4 Maverick 400B is the smaller model (400B vs 685B), 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, Llama 4 Maverick 400B or DeepSeek V3.2?
DeepSeek V3.2 ships under MIT, 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, Llama 4 Maverick 400B or DeepSeek V3.2?
Llama 4 Maverick 400B has the larger context window (976k vs 125k tokens), so it handles longer documents and codebases in a single prompt.