[Mistral Small 3](/models/mistralai--mistral-small-3) is a 24B dense model; [Mixtral 8x7B Instruct](/models/mistralai--mixtral-8x7b-instruct) is an MoE model activating ~13B of its 47B parameters per forward pass. On shared-infrastructure providers, they price within 10–20% of each other — but Mixtral 8x7B has been on the market longer and benefits from fierce supply competition, often landing cheaper on commodity endpoints. Throughput characteristics differ. Mixtral 8x7B's sparse activation means higher tokens-per-second on the same GPU footprint in throughput-optimized deployments. Mistral Small 3's smaller, denser architecture gives it more consistent time-to-first-token at low batch sizes, which matters for interactive use. **Where Mixtral 8x7B Instruct wins:** Batch inference at scale — translation, classification, summarization pipelines — where tokens-per-dollar is the primary metric and you're running large batch sizes. Its wide provider availability also means you can shop aggressively for spot pricing. **Where [Mistral Small 3](/models/mistralai--mistral-small-3) wins:** Applications requiring instruction following, structured output, or multi-turn coherence. Mistral Small 3's post-training improvements yield noticeably better tool-call accuracy and format adherence, making it the right default for function-calling APIs and lightweight agent scaffolds. Pick Mixtral 8x7B Instruct if you need maximum tokens-per-dollar on offline batch workloads and format consistency isn't critical. Pick Mistral Small 3 if you're building interactive features — chat, tool use, structured extraction — where instruction reliability outweighs marginal cost savings.