## Llama 3.3 70B Instruct vs Mistral Large 2 [Llama 3.3 70B Instruct](/models/meta--llama-3.3-70b-instruct) and [Mistral Large 2](/models/mistralai--mistral-large-2) are both positioned as high-quality 70B-range instruction models, but they differ in pricing and licensing. Llama 3.3 70B runs $0.20–$0.40/1M tokens at most providers; Mistral Large 2 typically costs $0.60–$2.00/1M tokens depending on provider tier. That 3–5× gap is significant at scale. On benchmarks, the two are close on English reasoning: both score in the 80–84% range on MMLU, and Mistral Large 2 edges ahead by 2–3 points on complex coding tasks (HumanEval). Llama 3.3 70B was explicitly tuned to match 405B-class performance on instruction following, which shows on IFEval benchmarks where it scores above 90%. Architecturally, Mistral Large 2 uses a 32K context window by default, while Llama 3.3 70B supports up to 128K context on providers that expose it. For RAG workloads with large retrieved contexts, that matters. **Where Llama 3.3 70B wins:** Cost-sensitive production deployments, long-context RAG, and English-language instruction tasks. The open weights also mean self-hosting is viable, removing vendor lock-in entirely. **Where Mistral Large 2 wins:** Complex multi-step code generation and tasks where Mistral's function-calling format is already integrated into your stack. Its tool-use reliability is marginally better on structured API tasks. Pick Llama 3.3 70B if cost or context length is a constraint. Pick Mistral Large 2 if your pipeline already uses Mistral's API format and the quality gap justifies 3–5× higher spend.