ModelRefs / Llama 4 Scout vs DeepSeek V3
Llama 4 Scout vs DeepSeek V3
Llama 4 Scout vs DeepSeek V3: side-by-side benchmarks, pricing, context windows, coding ability and deployment. Pick the right model for your stack.
Overview
DeepSeek V3: DeepSeek V3 is DeepSeek's December 2024 open-weights Mixture-of-Experts model, activating 37 billion of 671 billion total parameters per token across a 128,000-token context window. It ships under the MIT licence with published weights and a technical report, and DeepSeek reports its own evaluation table for the text chat model under an 8,000-token output limit.
Llama 4 Scout: Llama 4 Scout is Meta's efficiency-focused open-weights model with an exceptionally large 10M-token context window and vision support. It is optimized for long-document retrieval and low-cost inference at scale.
Context window: DeepSeek V3 accepts up to 128,000 tokens, Llama 4 Scout up to 10,000,000. Confirm the limit for your specific deployment channel before relying on it.
DeepSeek V3 vs Llama 4 Scout at a glance
| Attribute | DeepSeek V3 | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Released | 2024-12-26 | 2025-04-01 |
| Context window | 128,000 tokens | 10,000,000 tokens |
| Input price | $0.27/M tokens | $0.11/M tokens |
| Output price | $1.1/M tokens | $0.34/M tokens |
| Licence | MIT | Llama-4-Community |
| Self-hostable | Yes, open weights | Yes, open weights |
| Modalities | text | text, vision |
Reported on different variants
Both models are measured on these benchmarks, but not on the same variant of them, so the two numbers are not a like-for-like comparison. Each side is shown with the variant it was measured on.
| Benchmark | DeepSeek V3 | Llama 4 Scout | Reported |
|---|---|---|---|
| MMLU-Pro | 75.9 (exact match) | 74.3 (macro-average accuracy) | 2024-12-27 / 2025-04-01 |
A different task set or a different reported metric can move a score by more than the gap between two models. Use these as two separate findings, not as a ranking.
Where they differ most
- Coding: DeepSeek V3 67%, Llama 4 Scout 33%. DeepSeek V3 leads on this dimension.
- Reasoning: DeepSeek V3 72%, Llama 4 Scout 62%. DeepSeek V3 leads on this dimension.
- Cost Efficiency: DeepSeek V3 85%, Llama 4 Scout 94%. Llama 4 Scout leads on this dimension.
Capability scores are ModelRefs' own derived signals, not vendor claims or benchmark results. Validate against your own workload before relying on them.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Llama 4 Scout vs DeepSeek V3.
Frequently asked questions
Which is better, Llama 4 Scout or DeepSeek V3?
Llama 4 Scout and DeepSeek V3 target different workloads — see the benchmark and pricing tables for a side-by-side answer.
Is Llama 4 Scout cheaper than DeepSeek V3?
Compare $0.00011 vs $0.00027 on this page.