Llama 4 Maverick 400B vs Llama 4 Scout 109B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Llama 4 Maverick 400B | Llama 4 Scout 109B |
|---|---|---|
| Parameters | 400B | 109B |
| Author | Meta | Meta |
| License | Llama 4 Community | Llama 4 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 240 GB | 65 GB |
| VRAM at Q5 | 285 GB | 78 GB |
| VRAM at Q8 | 425 GB | 117 GB |
| VRAM at FP16 | 800 GB | 218 GB |
| Use cases | chat, general, vision, moe, multilingual | chat, general, vision, moe, multilingual |
Verdict
Llama 4 Maverick 400B is significantly larger (400B vs 109B), so expect higher quality but heavier VRAM and slower throughput.
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 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
Llama 4 Maverick 400B comes from Meta and Llama 4 Scout 109B from Meta. 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 109B parameters, Llama 4 Maverick 400B is the larger of the two. At Q4, Llama 4 Scout 109B fits in about 65 GB of VRAM versus 240 GB for the other — a 175 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Llama 4 Maverick 400B takes MMLU-Pro with 80 against 74 — a clear 6-point margin. For workloads weighted toward that benchmark, Llama 4 Maverick 400B is the stronger default.
On a typical mid-range GPU, Llama 4 Scout 109B 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 Scout 109B offers the bigger window (9765k vs 976k 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 Llama 4 Scout 109B requires Q4 ≈ 65 GB, Q5 ≈ 78 GB, Q8 ≈ 117 GB, FP16 ≈ 218 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 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 8 tokens/sec from Llama 4 Maverick 400B and 12 from Llama 4 Scout 109B, scaling up to 22 and 30 tokens/sec on high-end hardware.
Benchmark scores
Reported benchmarks for Llama 4 Maverick 400B: LMArena 70.85, MMLU-Pro 80.
Reported benchmarks for Llama 4 Scout 109B: MMLU-Pro 74.
Bottom line: which should you pick?
- 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 Llama 4 Maverick 400B for maximum headline quality.
- Pick Llama 4 Maverick 400B if MMLU-Pro performance is your priority (80 vs 74).
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 Llama 4 Maverick 400B and Llama 4 Scout 109B?
The headline differences: Llama 4 Maverick 400B is a 400B model and Llama 4 Scout 109B is 109B; their context windows differ (976k vs 9765k tokens). 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 Llama 4 Scout 109B 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); 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.
Is Llama 4 Maverick 400B or Llama 4 Scout 109B more capable?
On MMLU-Pro, Llama 4 Maverick 400B scores higher (80 vs 74), a 6-point advantage on this benchmark.
Which is faster, Llama 4 Maverick 400B or Llama 4 Scout 109B?
Llama 4 Scout 109B is the smaller model (109B vs 400B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Llama 4 Maverick 400B and Llama 4 Scout 109B use?
Llama 4 Maverick 400B is licensed under Llama 4 Community and Llama 4 Scout 109B under Llama 4 Community.
Which has the longer context window, Llama 4 Maverick 400B or Llama 4 Scout 109B?
Llama 4 Scout 109B has the larger context window (9765k vs 976k tokens), so it handles longer documents and codebases in a single prompt.