Llama 3.1 405B vs Mistral Large 2

On shared benchmark coverage, HumanEval has Llama 3.1 405B at 89 and Mistral Large 2 at 84.8, with Llama 3.1 405B ahead by 4.2 points; Chatbot Arena has Llama 3.1 405B at 1228 and Mistral Large 2 at 1265, with Mistral Large 2 ahead by 37 points; Massive Multitask Language Understanding has Llama 3.1 405B at 88.6 and Mistral Large 2 at 84, with Llama 3.1 405B ahead by 4.6 points. The largest visible gap is 37 points on Chatbot Arena, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on vision: Mistral Large 2, multimodal input: Mistral Large 2, function calling: Mistral Large 2, tool use: Mistral Large 2, and structured outputs: Mistral Large 2. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

Pricing coverage is uneven: Llama 3.1 405B has no token price sourced yet and Mistral Large 2 has $0.48/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Llama 3.1 405B when provider fit are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.