Capability · Comparison
Llama 3.3 70B vs Mistral Large 3
Llama 3.3 70B closed much of the gap to frontier models at the 70B-dense weight class; Mistral Large 3 pushes further on reasoning but trades away open weights in its commercial tier. Both are common picks for teams that want European data residency or fully self-hosted quality.
Side-by-side
| Criterion | Llama 3.3 70B | Mistral Large 3 |
|---|---|---|
| License | Llama Community License (open weights) | Mistral Commercial License (API-first) |
| Context window | 128,000 tokens | 256,000 tokens |
| Parameters | 70B dense | Not disclosed (~123B class) |
| MMLU | ~86% | ~88% |
| Reasoning quality | Good | Stronger — closer to frontier |
| Self-hosting | Yes, 70B fits on 2xH100 bf16 | On-prem available via EU contract |
| Pricing ($/M input, hosted) As of 2026-04. | ~$0.20 via Together/Groq | $2 via Mistral API |
| Data residency (EU) | Self-hosted anywhere | Mistral France EU-only |
| Multilingual | Strong | Excellent — European focus |
Verdict
Llama 3.3 70B is the more flexible option — truly self-hostable, massive fine-tune ecosystem, cheap on third-party inference. Mistral Large 3 is the stronger model on reasoning-heavy benchmarks and is the default for European enterprises that want sovereign-hosted top-tier quality. If you're in India on your own GPUs, Llama 3.3 70B is typically the right baseline; for EU regulated workloads, Mistral wins.
When to choose each
Choose Llama 3.3 70B if…
- You want open weights and full control over the model.
- You need broad ecosystem support — Together, Groq, Fireworks, Bedrock.
- You plan to fine-tune or LoRA on domain data.
- Cost per token matters and you're OK on hosted inference providers.
Choose Mistral Large 3 if…
- You need the strongest reasoning in this weight class.
- You need EU data residency with a French provider.
- You're OK with closed weights for the commercial tier.
- You need 256k context in a single call.
Frequently asked questions
Is Mistral Large 3 open-weight?
No — the current commercial tier is closed. Mistral has released open-weight models (Mistral Small, Codestral) under Apache 2.0 but Large 3 itself is API-only.
Which is better for coding?
Roughly tied. Codestral (Mistral's coding specialist) beats generic Llama 3.3 70B on code benchmarks; for general-purpose use with code, the two base models are close.
Can I run Llama 3.3 70B on my own hardware?
Yes — on 2xH100 (80GB) at bf16, or a single H100 with FP8/AWQ quantization. Consumer-grade deployment is realistic on 2x4090 with int4.
Sources
- Meta — Llama 3.3 70B — accessed 2026-04-20
- Mistral AI — Large — accessed 2026-04-20