Mistral Large 2 vs Llama 3.1 405B

Mistral Large 2 vs Llama 3.1 405B: which should you choose?

Mistral Large 2 — Mistral AI large language model (2024) with a 128K-token context window and an MMLU score of 84.0%.

Llama 3.1 405B — Meta large language model (2024) with a 128K-token context window and an MMLU score of 88.6%, released with open weights.

Mistral Large 2 vs Llama 3.1 405B: Llama 3.1 405B scores higher on the MMLU benchmark. Llama 3.1 405B leads general capability (MMLU 88.6% vs 84.0%).

Capability and reasoning

On MMLU — a 57-subject benchmark of general knowledge and reasoning — the Llama 3.1 405B scores 88.6% versus 84.0%. MMLU is a useful proxy for raw knowledge but does not capture instruction-following, coding, tool use, latency or safety, so treat it as one signal among several.

Context window

The Mistral Large 2 handles up to 128K tokens per request, which sets how much documentation, transcript or code it can reason over at once — decisive for retrieval-augmented and long-document workflows.

Pricing and access

Mistral Large 2 is proprietary api and Llama 3.1 405B is open weights. Proprietary models bill per token via API; open-weight models can be self-hosted, trading per-call cost for infrastructure you manage. For production, weigh throughput, rate limits and data-residency needs alongside headline price.

The verdict

Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.