TypeSafe AI Releases Jev: A System One Model That Returns Typed, Calibrated Decisions Instead of Text

TypeSafe AI launched Jev, a transformer-based 'System One' model API returning typed, calibrated decisions with probabilities, claiming 193x faster and 445x cheaper than LLMs.

TypeSafe AI released Jev, a transformer-based 'System One Model' that returns typed decisions with probabilities and confidence values instead of generated text. The API exposes three question primitives — Choice (up to 255 options), Score, and Noul (binary probability) — processed in parallel against a shared state and trained via Reinforcement Learning for Calibrated Decisions (RLCD). Pricing is $42 per billion input tokens with free output; vendor-run benchmarks claim 193.6x faster and 444.6x cheaper responses than GPT-5.6 Terra, though reference answers come from averaging other models and gains are unverified. Jev is in hosted early access behind a waitlist with no published weights, parameter count, or self-hosting option; community projects include agent guardrails, browser and phone agents, and Postgres natural-language filters.