Capability · Comparison
DeepSeek V3 vs Llama 3.1 405B
DeepSeek V3 and Llama 3.1 405B represent the two open-weight peaks of 2024-2025: V3 as a 671B-parameter MoE that activates only ~37B per token, and 405B as a fully dense frontier model from Meta. Both remain widely deployed in 2026 as the baselines that open-source reasoning and coding models built on.
Side-by-side
| Criterion | DeepSeek V3 | Llama 3.1 405B |
|---|---|---|
| Architecture | MoE: 671B total, 37B active | Dense 405B |
| Context window | 128,000 tokens | 128,000 tokens |
| License | DeepSeek License (commercial OK) | Llama 3.1 Community License |
| Coding (HumanEval) | ~90% | ~85% |
| Math (MATH) | ~90% | ~73% |
| Inference cost per token | Low — 37B active parameters | High — all 405B active |
| Required hardware (bf16) | ~1.3TB weights, 8xH100 minimum | ~810GB weights, 8xH100 minimum |
| Multilingual | Strong, especially CJK | Strong, especially EU languages |
| Ecosystem (fine-tunes) | Large (Chinese ecosystem) | Very large (global) |
Verdict
V3 is the more technically elegant model — MoE architecture gives it a serving-cost advantage and its coding/math numbers are ahead. Llama 3.1 405B's advantage is simplicity and ecosystem: it's dense, every inference engine supports it first-class, and there's a massive fine-tune ecosystem around it. For new projects in 2026, V3 is usually the better bet; for brownfield Llama shops, 405B is still fine.
When to choose each
Choose DeepSeek V3 if…
- You need strong open-weight coding or math performance.
- Per-token inference cost matters at scale.
- You want SOTA open-weight general quality.
- You're OK running MoE inference (vLLM / SGLang have mature support).
Choose Llama 3.1 405B if…
- You need simple dense deployment on existing Llama infra.
- You rely on the Llama ecosystem of fine-tunes and safety filters.
- You need strong European-language performance.
- Your stack is tuned for dense-transformer inference kernels.
Frequently asked questions
Which is cheaper to run — V3 or Llama 405B?
V3, materially — activating only 37B parameters per token means lower GPU memory bandwidth per token and typically 2-3x higher throughput on the same hardware.
Is V3 really open-weight?
Yes — weights are freely downloadable under the DeepSeek License, which permits commercial use. It's genuinely open, though not OSI-approved.
Should I still pick 405B in 2026?
Only if you're already on Llama-specific infrastructure or you need a fully dense model. For new deployments, V3 or newer open-weight MoE models (Llama 4, Qwen 2.5) are usually better.
Sources
- DeepSeek-V3 Technical Report — accessed 2026-04-20
- Meta — Llama 3.1 announcement — accessed 2026-04-20