[Llama 3.1 405B Instruct](/models/meta--llama-3.1-405b-instruct) is a 405-billion-parameter dense transformer; [Mistral Large 2](/models/mistralai--mistral-large-2) sits at roughly 123B parameters. That size gap drives most of the cost story: 405B typically prices at $2–5/M input tokens across providers, while Mistral Large 2 runs $2–3/M — a meaningful difference that widens further at scale. On throughput, 405B delivers roughly 20–35 tok/s per request on A100 clusters; Mistral Large 2 reaches 40–70 tok/s, nearly double, because fewer parameters fit a tighter GPU footprint. **Where 405B wins:** Long-context reasoning tasks — multi-document synthesis, complex code generation spanning thousands of lines, or multi-step agentic chains — benefit from the raw parameter depth. Teams running nightly batch jobs where latency matters less than answer quality tend to see measurable gains here. **Where Mistral Large 2 wins:** Latency-sensitive APIs (chat, autocomplete, retrieval-augmented generation with short contexts) favor the smaller model. Sub-second p50 latency is achievable on Mistral Large 2 at practical concurrency; 405B often pushes p50 above 2–3 seconds under load. Mistral Large 2 also offers stronger multilingual support across 80+ languages, relevant for European or APAC user bases. **Bottom line:** Pick Llama 3.1 405B Instruct if your workload is batch-oriented, quality-critical, and tolerates higher per-token spend. Pick Mistral Large 2 if you need real-time response latency, multilingual coverage, or want to cut inference cost by 30–50% with acceptable quality trade-offs.