Capability · Comparison
Llama 4 Maverick vs Llama 4 Scout
Llama 4 is Meta's first fully-MoE generation. The family ships two public variants: Maverick (larger, stronger on quality benchmarks) and Scout (smaller, but with a headline 10M-token context window designed for document analysis and agentic workflows). Both are open-weights under the Llama Community License.
Side-by-side
| Criterion | Llama 4 Maverick | Llama 4 Scout |
|---|---|---|
| Architecture | MoE — 400B total, 17B active per token (128 experts) | MoE — 109B total, 17B active per token (16 experts) |
| Context window | 1,000,000 tokens | 10,000,000 tokens |
| Reasoning benchmarks | Stronger — rivals GPT-4o class | Solid — behind Maverick but excellent for size |
| Coding benchmarks | Strong | Good |
| Multimodality | Text + vision | Text + vision |
| Memory footprint (bf16) | ~800GB — needs 16x H100 at least | ~220GB — fits 4x H100 80GB |
| Inference latency (typical) | Moderate | Fast |
| Hosted API pricing (as of 2026-04) | ~$0.80/M input (Together, Fireworks) | ~$0.25/M input (Together, Fireworks) |
| Best fit | Replace GPT-4o/Sonnet-class workloads with open weights | Document-heavy, long-context agents on a budget |
Verdict
Maverick is Meta's 'quality' flagship — it's the open-weights answer to GPT-4o-class closed models. Scout is more interesting in many real-world agentic situations because its 10M-token context and faster inference make it a genuinely novel tool for long-document analysis, codebase-scale Q&A, and agents that accumulate lots of context. Both are MoE-efficient. For general chat and reasoning, reach for Maverick. For document intelligence and long-agent traces, reach for Scout. Cost-sensitive production pipelines often route by context length: Scout if >200k tokens, Maverick otherwise.
When to choose each
Choose Llama 4 Maverick if…
- You want the highest open-weights quality in Meta's 2026 line.
- Reasoning and coding benchmarks drive your decision.
- You have GPU budget for ~16x H100 deployment.
- You're replacing a GPT-4o-class closed-source dependency.
Choose Llama 4 Scout if…
- You need 1M+ token context in an open-weights model.
- Your use case is long-doc QA, codebase analysis, or long-trace agents.
- Latency and cost matter more than top-end reasoning.
- You can deploy on 4x H100 (or an equivalent inference service).
Frequently asked questions
Is the 10M context on Scout real?
Yes — Llama 4 Scout was trained with positional encodings that support a 10M-token context. In practice, retrieval quality over such long contexts depends heavily on the task; third-party needle-in-haystack tests confirm it works at multi-million-token lengths, but quality degrades past a few hundred thousand tokens on complex reasoning.
Can I fine-tune Llama 4 Scout on my data?
Yes, under the Llama Community License. LoRA and QLoRA are common; full fine-tuning is expensive because of the 109B parameters total. Axolotl, TorchTune, and Unsloth all support Llama 4 architectures as of 2026-04.
Why does Maverick need so much more VRAM than Scout if only 17B are active?
MoE models still need ALL experts resident in memory even though only a subset activates per token. Maverick has 128 experts at 3.3B each; Scout has 16 experts. The active-parameter count drives compute, not memory.
Sources
- Meta — Llama 4 model card — accessed 2026-04-20
- Hugging Face — Llama 4 collection — accessed 2026-04-20