Mistral AI

Mistral Small 4

Maker
Mistral AI
Origin
France
Released
Mar 16, 2026
Category
Language Models

Strengths & weaknesses

  • One open-weight model that combines reasoning, image input, and agentic coding, under Apache 2.0
  • Mixture-of-experts design activates only 6.5B of 119B parameters per token, keeping inference efficient
  • A small-tier model: trails frontier flagships on the hardest reasoning tasks
  • At 119B total parameters, self-hosting needs far more memory than earlier Small models

Evaluation

Awaiting evaluation
  • Reasoningw30—

    No independent measurement yet

  • Accuracyw20—

    No independent measurement yet

  • Writingw10—

    No independent measurement yet

  • Long contextw10—

    No independent measurement yet

  • Cost efficiencyw20—

    No independent measurement yet

  • Deployabilityw10—

    No independent measurement yet

w = weight, each criterion’s share of the overall score. Missing marks don’t count for or against. How scoring works

Enterprise fit

Highest-ROI use cases

Where Mistral Small 4 fits inside a company, ranked by the strength of published ROI evidence for each use case.

All 100 enterprise use cases →
  1. 01
    Research, analysis & reportingControlled trial
    Strategy, consulting, finance
    • ▸ Built for this kind of work (Language Models)
  2. 02
    Marketing, content & salesControlled trial
    Marketing & sales
    • ▸ Built for this kind of work (Language Models)
  3. 03
    Customer supportField study
    Customer service
    • ▸ Built for this kind of work (Language Models)

More in Language Models

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OpenAI

GPT-5.6

7.2/10
  • Best-in-class agentic reasoning, with search, code execution, and computer use in one API
  • Disciplined, low-hallucination output on long, multi-step tasks
  • Long-context requests above roughly 272K tokens get repriced sharply higher
  • Slower to produce a first answer than most rivals at max reasoning
Anthropic

Claude Opus 5

7.1/10
  • Anthropic's recommended model for complex, high-stakes work, with strong reasoning-to-cost
  • Zero-data-retention eligible, useful for regulated or enterprise deployments
  • Sits below the flagship tier in branding despite strong practical scores
  • Costs meaningfully more per token than the Sonnet tier for everyday tasks
Anthropic

Claude Sonnet 5

6.4/10
  • Fast and capable, priced for everyday production use
  • Strong default for coding and long documents without Opus-level cost
  • Enabling maximum thinking mode can quietly balloon token spend
  • Trails Opus on the hardest multi-step reasoning problems
Anthropic

Claude Haiku 4.5

5.7/10
  • Cheapest Claude tier, well suited to high-volume, simple tasks
  • Low latency, good for chat-style and classification workloads
  • Smaller context window (200K tokens) than Sonnet 5 and Opus 5 (1M)
  • Noticeably weaker on hard reasoning than Sonnet or Opus