Mistral Small 3.2

Mistral Small 3.2 (24B) is a scaled-up version of the compact Small 3.2 family, designed to maximize capability without losing the speed and efficiency benefits that make the “Small” line appealing. With 24 billion parameters, it delivers noticeably better reasoning, summarization, and multilingual performance than lighter variants, while retaining low-latency inference suitable for real-time assistants.

The model is instruction-tuned to follow prompts cleanly, generate step-by-step explanations, and produce schema-consistent JSON or function calls, which makes it straightforward to plug into automation pipelines, retrieval-augmented generation setups, and agent frameworks. Its long-context handling allows it to process extended conversations, multi-document workflows, or repo-scale codebases with reliable coherence.

Engineered for deployment flexibility, it runs efficiently on modern GPU servers with quantization options for memory savings, and it can be adapted with LoRA or domain-specific fine-tuning. Typical uses include customer and employee copilots, analytics explainers, enterprise chatbots, and code assistants where you want stronger accuracy and reasoning than smaller models but at a fraction of the serving cost of Large 2.