Mistral Small 3

Mistral Small 3 decisively raises the bar within the "small" LLM category (<70B parameters), packing impressive capabilities into its compact 24B-parameter footprint. It achieves notable performance metrics across reasoning, instruction-following, and coding benchmarks, highlighted by a robust pass@1 score of 84.8% on HumanEval—nipping closely at the heels of significantly larger models like Llama-3.3-70B (85.4%) and Qwen-2.5-32B (90.9%). Human evaluation further reinforces its practical strengths: evaluators consistently preferred Mistral Small 3 over models such as Gemma-2-27B and Qwen-2.5-32B in over 50% of head-to-head comparisons, underscoring its genuine user appeal. Efficiency emerges as Mistral Small 3's standout quality. With a substantial 32k context window, the model caters effortlessly to expansive, context-rich applications. Critically, its latency optimization delivers 150 tokens per second on mainstream hardware, making it ideal for real-time, conversational applications—think responsive virtual assistants or low-latency function-calling agents. Remarkably, quantization enables deployment on accessible hardware like a single RTX 4090 GPU or a 32GB RAM MacBook, democratizing access to high-caliber LLM performance for local and privacy-sensitive use cases. In sum, Mistral Small 3 isn't just punching above its weight—it's setting a new efficiency-performance benchmark. It's a particularly compelling open-source alternative to proprietary models like GPT-4o-mini, offering comparable real-world effectiveness with dramatically lower computational demands.