Mistral Large 2 is a 123B dense transformer; Mixtral 8x22B Instruct is a sparse mixture-of-experts model activating ~39B of its 141B total parameters per token. That architectural gap drives most of the pricing and latency divergence you'll see across providers. Mixtral 8x22B typically prices 20–35% lower per million output tokens than Mistral Large 2, because MoE inference requires fewer active FLOPs per step. Latency flips the advantage. Mistral Large 2's dense forward pass is more predictable under load: time-to-first-token stays tighter at high concurrency. Mixtral 8x22B's expert routing adds variable overhead, which shows up as tail-latency spikes on shared GPU clusters. **Where [Mixtral 8x22B Instruct](/models/mistralai--mixtral-8x22b-instruct) wins:** High-volume batch workloads — document summarization pipelines, offline classification, bulk translation — where you're cost-sensitive and latency SLAs are loose (>5 s P99 acceptable). The lower active-parameter count also means faster cold-start on self-hosted deployments. **Where [Mistral Large 2](/models/mistralai--mistral-large-2) wins:** Interactive applications, multi-turn agents, and structured-output tasks where P95 latency matters. Its dense architecture also shows stronger instruction-following consistency on long-context inputs (≥32 K tokens), making it the safer choice for agentic loops that parse or generate tool calls. Pick Mistral Large 2 if you need predictable latency and reliable instruction following in production agents. Pick Mixtral 8x22B Instruct if you're optimizing cost on throughput-heavy offline pipelines and can tolerate higher tail latency.