Overview
- Jev is a purpose-built model that takes a state and a typed question and returns calibrated probabilities for closed answer spaces using three primitives: Choice (pick one of N), Score (position on ordered levels), and Noul (yes/no probability).
- Early production use by agent platforms such as Octomind shows answers return in tens-to-hundreds of milliseconds and cost roughly $0.042 per million input tokens, making many small judgments orders of magnitude cheaper than chat models.
- Engineers report concrete integration rules needed for correct results, including using the argmax of returned probabilities for Score buckets, treating the supplied option list as part of the prompt, and interpreting the 'confidence' field as peakiness of the distribution rather than inherent uncertainty.
- Safe deployments keep Jev as a veto or hold-back tool, not an approver, and design fallback paths to chat models so mistakes cause recoverable extra work rather than silent approvals.
- Adopters should empirically validate Jev’s calibration on their own data and expect more automated checks as decisions get cheaper, a behavioral shift TypeSafe named after the Jevons paradox.