DeepSeek V3.2 vs Llama 4 Scout 109B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | DeepSeek V3.2 | Llama 4 Scout 109B |
|---|---|---|
| Parameters | 685B | 109B |
| Author | DeepSeek | Meta |
| License | MIT | Llama 4 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 410 GB | 65 GB |
| VRAM at Q5 | 490 GB | 78 GB |
| VRAM at Q8 | 735 GB | 117 GB |
| VRAM at FP16 | 1370 GB | 218 GB |
| Use cases | chat, general, moe | chat, general, vision, moe, multilingual |
Verdict
DeepSeek V3.2 is significantly larger (685B vs 109B), 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 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.
About Llama 4 Scout 109B
Meta's compact Llama 4 MoE — 109B total, 17B active, natively multimodal, with an unprecedented 10M token context. Fits on a single H100. Strengths: 10M token context — unmatched among open models, Runs on a single H100 thanks to MoE sparsity, Native multimodal input — no separate vision adapter needed, 17B active parameters keeps inference fast.
How they compare
DeepSeek V3.2 comes from DeepSeek and Llama 4 Scout 109B from Meta, they belong to the DeepSeek 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 685B vs 109B parameters, DeepSeek V3.2 is the larger of the two. At Q4, Llama 4 Scout 109B fits in about 65 GB of VRAM versus 410 GB for the other — a 345 GB difference that matters on consumer GPUs.
The two models target different sweet spots: DeepSeek V3.2 is tuned for chat, general, moe, while Llama 4 Scout 109B 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, Llama 4 Scout 109B pushes roughly 12 tokens/sec versus 5, so it is the more responsive choice for interactive or high-volume use. For long-context work, Llama 4 Scout 109B offers the bigger window (9765k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, DeepSeek V3.2 requires Q4 ≈ 410 GB, Q5 ≈ 490 GB, Q8 ≈ 735 GB, FP16 ≈ 1370 GB, while Llama 4 Scout 109B requires Q4 ≈ 65 GB, Q5 ≈ 78 GB, Q8 ≈ 117 GB, FP16 ≈ 218 GB. In practice DeepSeek V3.2 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, DeepSeek V3.2 needs roughly 512 GB of system RAM to run on CPU and Llama 4 Scout 109B about 100 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 5 tokens/sec from DeepSeek V3.2 and 12 from Llama 4 Scout 109B, scaling up to 15 and 30 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Llama 4 Scout 109B: MMLU-Pro 74.
Bottom line: which should you pick?
- Pick DeepSeek V3.2 if you need a permissive (MIT) license for commercial deployment.
- Pick Llama 4 Scout 109B for long-context work (up to 9765k tokens).
- Pick Llama 4 Scout 109B for lower VRAM and faster inference; pick DeepSeek V3.2 for maximum headline quality.
- Pick Llama 4 Scout 109B 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 DeepSeek V3.2 and Llama 4 Scout 109B?
The headline differences: DeepSeek V3.2 is a 685B model and Llama 4 Scout 109B is 109B; their context windows differ (125k vs 9765k tokens); they ship under different licenses (MIT 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 DeepSeek V3.2 and Llama 4 Scout 109B run on a 24 GB GPU?
At a Q4 quantization, DeepSeek V3.2 needs about 410 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 4 Scout 109B needs about 65 GB and needs more than 24 GB. Llama 4 Scout 109B is the lighter option for tight VRAM budgets.
Which is faster, DeepSeek V3.2 or Llama 4 Scout 109B?
Llama 4 Scout 109B is the smaller model (109B 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, DeepSeek V3.2 or Llama 4 Scout 109B?
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, DeepSeek V3.2 or Llama 4 Scout 109B?
Llama 4 Scout 109B has the larger context window (9765k vs 125k tokens), so it handles longer documents and codebases in a single prompt.