Mistral Small 3.5Mistral Small 3.5
- Maker
- Mistral AI
- Released
- Not yet confirmed
Strengths & weaknesses
- +Efficient, low-cost option that still handles most everyday tasks well
- +Open weights and Apache 2.0 licensing for self-hosting
- −Clearly outclassed by frontier models on complex reasoning
- −Multimodal support lags behind larger Mistral and rival models
Evaluation
Awaiting evaluationReasoningw25—
Accuracyw20—
Long contextw15—
Speedw10—
Cost efficiencyw15—
Deployabilityw15—
w = weight, each criterion’s share of the overall score. How scoring works
- +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'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
- +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
- +Cheapest Claude tier, well suited to high-volume, simple tasks
- +Low latency, good for chat-style and classification workloads
- −No adaptive or extended thinking mode
- −Noticeably weaker on hard reasoning than Sonnet or Opus