[Llama 3.1 70B Instruct](/models/meta--llama-3.1-70b-instruct) is a 70B dense model; [Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) uses a mixture-of-experts architecture with 141B total parameters but only ~39B active per token. That MoE design makes serving economics non-obvious: Mixtral 8x22B requires loading the full 141B weight set into memory (higher GPU RAM cost per replica), but compute per forward pass stays closer to a 39B dense model. In practice, hosted pricing for Mixtral 8x22B runs $0.90–$2/M input tokens versus $0.25–$0.90/M for Llama 3.1 70B — Llama 3.1 70B is consistently cheaper. Throughput skews similarly: Llama 3.1 70B delivers 60–100 tok/s; Mixtral 8x22B typically achieves 40–70 tok/s due to MoE routing overhead and memory bandwidth constraints. **Where Llama 3.1 70B wins:** Cost-sensitive, high-volume APIs. At 2–3× lower per-token cost, it's the practical default for RAG pipelines, classification, and summarization at scale. Provider breadth is also wider, giving better SLA optionality. **Where Mixtral 8x22B wins:** Code generation and structured output tasks, where the model's strong performance on coding benchmarks and its 64K context window provide an edge. Teams running long-context technical analysis — code review, log analysis, multi-file diffs — often find Mixtral 8x22B's quality justifies the premium. **Bottom line:** Pick Llama 3.1 70B Instruct for cost-optimized, general-purpose workloads. Pick Mixtral 8x22B Instruct if you need longer context windows and stronger coding performance and can absorb the 2–3× pricing difference.