Llama 3 8B Instruct vs Mistral Large 2 (2407)

On shared benchmark coverage, MMLU PRO has Llama 3 8B Instruct at 40.5 and Mistral Large 2 (2407) at 65.9, with Mistral Large 2 (2407) ahead by 25.4 points; HumanEval has Llama 3 8B Instruct at 68.2 and Mistral Large 2 (2407) at 92, with Mistral Large 2 (2407) ahead by 23.8 points; Massive Multitask Language Understanding has Llama 3 8B Instruct at 76.9 and Mistral Large 2 (2407) at 84, with Mistral Large 2 (2407) ahead by 7.1 points. The largest visible gap is 25.4 points on MMLU PRO, 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 JSON / Tool use: Mistral Large 2 (2407). 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 8B Instruct lists $0.02/1M input and $0.05/1M output tokens on the cheapest tracked provider, while Mistral Large 2 (2407) lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3 8B Instruct lower by about $0.77 per million blended tokens. Availability is 17 providers versus 7, so concentration risk also matters.

Choose Llama 3 8B Instruct when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Mistral Large 2 (2407) when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.