Llama 3.1 70B Instruct vs Mistral Large 2

On shared benchmark coverage, HumanEval has Llama 3.1 70B Instruct at 84.1 and Mistral Large 2 at 84.8, with Mistral Large 2 ahead by 0.7 points; Massive Multitask Language Understanding has Llama 3.1 70B Instruct at 86 and Mistral Large 2 at 84, with Llama 3.1 70B Instruct ahead by 2 points; HellaSwag has Llama 3.1 70B Instruct at 94.2 and Mistral Large 2 at 93.8, with Llama 3.1 70B Instruct ahead by 0.4 points. The largest visible gap is 2 points on Massive Multitask Language Understanding, 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, and tool use: Mistral Large 2. Both models share structured outputs, 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.

For cost, Llama 3.1 70B Instruct lists $0.4/1M input and $0.4/1M output tokens, while Mistral Large 2 lists $0.48/1M input and $2.4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $0.66 per million blended tokens. Availability is 11 providers versus 4, so concentration risk also matters.

Choose Llama 3.1 70B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Mistral Large 2 when vision-heavy evaluation are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.