What are the best alternatives to Mistral Small 4?

The best alternatives to Mistral Small 4 include Mistral Medium 3.5, Claude Opus 4.8, Llama (Meta AI), Mistral. Each offers unique features for different use cases.

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Mistral Small 4

Mistral's unified open-source model — reasoning + vision + coding, Apache 2.0

freemiumDR 87Open weights under Apache 2.0 license — free to download, self-host, fine-tune, and use commercially. Available via Mistral API (Mistral Small tier pricing) and Le Chat (free + Pro plans).View full pricing →

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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.

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Key Features

✓119B total parameters, 6B active per token (MoE: 128 experts, 4 active)

✓256k token context window

✓Unified reasoning, vision, and coding in a single model

✓Configurable reasoning effort: reasoning_effort='none' (fast) or 'high' (deep)

✓Native image input support (text + vision in one model)

✓Apache 2.0 license — permissive commercial use, no additional restrictions

✓40% reduction in end-to-end latency vs Mistral Small 3

✓3× higher throughput vs Mistral Small 3 (throughput-optimized setup)

✓Beats GPT-OSS 120B on AA LCR and LiveCodeBench with shorter outputs

✓Runs on vLLM, llama.cpp, SGLang, and Transformers

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