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Mistral Small 4
Mistral's unified open-source model — reasoning + vision + coding, Apache 2.0
0Visit Mistral Small 4
https://mistral.ai/news/mistral-small-4/
About Mistral Small 4
Mistral's first unified open-source model, released March 16, 2026. A 119B MoE model (6B active parameters per token) that merges reasoning (Magistral), multimodal vision (Pixtral), and agentic coding (Devstral) into a single Apache 2.0 model. 256k context window. 40% faster and 3× higher throughput than Mistral Small 3. Beats GPT-OSS 120B on coding and reasoning benchmarks while generating shorter outputs.
Key Features
Mistral Small 4 Pros & Cons
✅ Pros
- +Apache 2.0 license — genuinely open source with no commercial restrictions, unlike most frontier-class open models
- +MoE architecture: 6B active parameters per token means inference is far cheaper than a dense 119B model
- +Unified reasoning + vision + coding eliminates model routing overhead for multi-task pipelines
- +Configurable reasoning_effort per API request — tune quality vs. latency without switching models
- +40% faster than Small 3 and 3× higher throughput — a meaningful improvement for high-volume workloads
- +Beats GPT-OSS 120B on coding and reasoning benchmarks while producing shorter, more efficient outputs
- +Available on vLLM, llama.cpp, SGLang — integrates with existing inference infrastructure
⚠️ Cons
- −Self-hosting still requires serious GPU resources: minimum 4× HGX H100, 2× HGX H200, or 1× DGX B200
- −Newer and less battle-tested in production than GPT-4o or Claude Sonnet at scale
- −Vision benchmarks not fully published — Mistral hasn't released detailed Pixtral-equivalent image task scores
- −For maximum reasoning quality on hard math and research tasks, frontier-only models (o3, Claude Opus 4.8) still lead
Who Is Mistral Small 4 Best For?
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