AI Model2026-05-17

Mistral

Also known as: Mistral / Mistral AI / Mistral Small / Mistral Large

Open-weights LLMs from French AI startup Mistral AI. Mistral Small 4 integrates reasoning, multimodal, and code into a 119 B MoE model with strong European-language support.

Overview

Mistral Small 4 uses a MoE architecture to combine reasoning, vision, and code generation in a single model, released under Apache 2.0 for easy commercial use. See Mistral Small 4 guide.

Strengths

Designed with EU AI Act compliance in mind, driving adoption in regulated industries like finance and healthcare. Runs locally to safeguard privacy while delivering high-quality reasoning.

Mistral Small 4, released March 2026, unifies reasoning, multimodal vision, and agentic coding in a 119B MoE model under Apache 2.0. Supports 11 languages including Japanese. Full specs, setup guide, and model comparisons.

Local LLM Landscape April 2026 — Top 10 Open-Source Models Comprehensive Comparison [Ollama Guide]

Comprehensive comparison of the top 10 local LLMs as of April 2026. Covers SWE-bench scores, Japanese language performance, VRAM requirements, Ollama commands, and licensing for Gemma 4, Llama 4, Qwen 3.5, GLM-5.1, Kimi K2.5, MiniMax M2.5, and more.

Claude Alternative Local LLM Comparison 2026 — Qwen 3.5, Mistral Small 4, DeepSeek R1 & Gemma 4 Reviewed

Following Anthropic Claude restrictions, comprehensive comparison of local LLMs including Qwen 3.5-9B, Mistral Small 4, DeepSeek R1, Gemma 4, and Llama 4. Detailed analysis of Japanese performance, hardware requirements, and use-case recommendations.

Shieldstral 1.0 3B Explained: Mistral's Open-Weight Multimodal Moderation Model

Shieldstral 1.0 3B is Mistral AI's Apache 2.0 moderation model, released Aug 4, 2026. It needs ~16GB VRAM in BF16 and screens both text and images together.

Gemma 4 Performance Benchmark — Compared Against Llama 4, Qwen, Mistral, and DeepSeek on Quality, Speed, and Cost-Efficiency [2026 Open-Weights LLM Showdown]

A 2026 Q2 performance benchmark of Gemma 4 (E2B / E4B / 26B MoE / 31B Dense) against the major open-weights peers — Llama 4, Qwen 3.5, Mistral, and DeepSeek — across MMLU-Pro, GPQA, HumanEval, MATH-500, and MT-Bench. Adds throughput (tokens / s), memory efficiency (quality per GB VRAM), cost per million tokens, Japanese-language performance, native function calling, and Apache 2.0 / MIT / commercial-use licensing as of May 2026, plus a use-case selection matrix for in-house LLM, edge AI, coding assistants, and RAG.

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