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Mistral AI · Mistral
No. 22
Mistral Small 4
Tiny Apache-2.0 multimodal model unifying reasoning, vision and coding.
Field notes
- Context window
- 262Ktokens
- Max output
- 33Ktokens
- Input price
- $0.15/ 1M tokens
- Output price
- $0.60/ 1M tokens
- Modalities
- Text, Image
- Knowledge cutoff
- —
- Released
- Mar 2026
- Parameters
- 24B
- License
- Apache 2.0
- Approx. speed
- ~169 tok/s
Overview
What this model is
Mistral Small 4 (2603) is a compact Apache-2.0 model that merges Mistral's previously separate Magistral (reasoning), Pixtral (vision) and Devstral (coding) lines into one small, cheap set of weights with an optional reasoning mode. It is one of the best-value small multimodal models and fully unrestricted for commercial use, but it is not built for hard, long agentic coding. Ideal for edge, local, and high-volume deployments where openness and cost dominate.
Strengths
- Fully open Apache 2.0 license
- Very fast output (~169 t/s) and cheap
- Unified reasoning + vision + coding
- 256K context, self-hostable on modest hardware
Trade-offs
- Absolute capability below larger models
- Agentic reliability limited
- Weaker on hardest reasoning/coding
- Small-model recall limits on long context
Best for
- Local and edge deployment
- High-volume cheap inference
- Fine-tuning with no license friction
- Lightweight multimodal tasks
Not ideal for
- Complex agentic coding
- Frontier reasoning workloads
Capabilities
Capability profile
Normalized 0–100 scores, comparable across the whole catalog.
- Reasoning
- 66
- Coding
- 64
- Math
- 64
- Writing
- 72
- Knowledge
- 72
- Speed
- 90
- Agentic
- 58
- Vision
- 66
- Multilingual
- 80
- Long Context
- 76
Benchmarks
How it scores
Public benchmark results, with independent third-party results where available. Bars normalize percentages to 100 and Elo ratings to a 1500 ceiling.
Independent index
Artificial Analysis Intelligence Index
Composite of ~9–10 independent evals · Artificial Analysis
21/ 100
BenchmarkResult
Artificial Analysis Intelligence Indexindependent
GeneralArtificial Analysis
21%
τ-benchindependent
Agenticτ-bench
65.8%