[Gemma 2 27B IT](/models/google--gemma-2-27b-it) is Google DeepMind's instruction-tuned 27B dense model from the Gemma 2 family, trained with knowledge distillation from larger models and interleaved sliding-window attention. [Mistral Small 3](/models/mistralai--mistral-small-3) sits in the sub-25B tier with Mistral's characteristic efficiency focus — low latency, high throughput, and competitive reasoning at the small-model price point. Both models target the cost-conscious segment, with typical hosted rates under $0.20/M input tokens at competitive providers. Gemma 2 27B IT has a slight size advantage and frequently leads on knowledge benchmarks (MMLU ~74–76%) due to distillation benefits from larger Gemma models. Mistral Small 3 is generally faster to first token on providers with dedicated Mistral infrastructure. Gemma 2 27B IT performs well on document understanding and summarization tasks where its sliding-window attention handles long contexts more coherently than vanilla attention at the same scale. It also benefits from Google's safety tuning investments, making it a reliable choice for consumer-facing applications with stricter content filtering requirements. Mistral Small 3 earns its position on function-calling and structured-output pipelines — Mistral has prioritized tool-use reliability across its model family, and Small 3 inherits those improvements. For high-volume API pipelines where you're calling the model hundreds of times per minute and need reliable JSON extraction or tool dispatch, Mistral Small 3's lower latency and consistent structured-output behavior reduce pipeline error rates. Pick Gemma 2 27B IT if knowledge-intensive tasks, long document handling, or Google's safety-tuning profile matches your application requirements. Pick Mistral Small 3 if first-token latency, function-calling reliability, and high-throughput structured-output pipelines are the primary requirements.