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# decision models → bias → jev → interpretability → classification

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Jev introduces a new shape of LLM—System One, aka Decision Models

TypeSafe AI released Jev, a model that takes text input but outputs confidence scores and probability distributions instead of generated text. It's designed for classification tasks like spam detection and ranking, costs $0.042 per million input tokens with free output, and has already spawned open-source recreations and creative experiments.
  • Jev returns structured probabilistic outputs (yes/no scores, rankings, category choices) rather than text, making it fast and cheap—you only pay for input tokens, not output.
  • The model trades interpretability for simplicity: you get a floating-point score but no explanation of which signals drove the decision, raising serious concerns about hidden bias in high-stakes use cases like hiring.
  • The community has already built unconventional projects with it (a chat model, a game player, a left-pad implementation) and created open-weight alternatives like Kev within days of release.
Last saved Sep 22, 2026 · 4 min read
jev decision models classification + structured outputs interpretability bias