Mistral Small
By Mistral AI
Mistral Small is a cost-efficient, mid-sized large language model in Mistral AI's commercial model lineup, designed to deliver strong performance on common tasks like summarization, classification, and simple reasoning at lower latency and…
Definition
Mistral Small is a cost-efficient, mid-sized large language model in Mistral AI's commercial model lineup, designed to deliver strong performance on common tasks like summarization, classification, and simple reasoning at lower latency and cost than the company's larger flagship models.
Overview
Mistral AI, the French AI lab founded in 2023 by former DeepMind and Meta researchers, structures its API model lineup around a capability/cost tradeoff, with Mistral Small positioned as the efficient workhorse tier below Mistral Medium and the flagship Mistral Large. Mistral Small was first introduced in 2024 alongside Mistral Large as part of the company's shift toward a mixed open-weight and proprietary release strategy, after earlier fully open releases like Mistral 7B and Mixtral 8x7B established the company's reputation. Subsequent versions, including Mistral Small 3 (released as an open-weight 24-billion-parameter model in early 2025) and later updates, emphasized being competitive with much larger models — Mistral AI specifically marketed Mistral Small 3 as comparable to models three times its size on certain benchmarks, while running with much lower latency, making it attractive for local or on-premises deployment as well as high-throughput API use. Unlike Mistral Large, several Small-tier releases have been made available under open licenses (such as Apache 2.0), aligning with Mistral AI's broader strategy of open-sourcing smaller and mid-sized models while monetizing the largest, most capable models and enterprise services. Mistral Small is commonly used for tasks that don't require frontier-level reasoning — such as classification, extraction, and conversational assistants — where cost and latency matter more than squeezing out the last few points of benchmark accuracy. It competes with other efficient mid-sized models such as OpenAI's GPT-4o mini, Google's Gemini Flash tier, and Anthropic's Claude Haiku models, all of which occupy a similar cost/latency niche within their respective vendor ecosystems.
Key Features
- Positioned as the efficient, low-latency tier below Mistral Medium and Mistral Large
- Several releases (e.g., Mistral Small 3) available as open weights under Apache 2.0
- Marketed as competitive with significantly larger models on select benchmarks
- Suitable for local, on-premises, or edge deployment given its smaller size
- Available via the Mistral API, La Plateforme, and major cloud marketplaces
- Designed for high-throughput tasks like classification, extraction, and simple chat
- Part of Mistral AI's mixed open/proprietary release strategy