Last week, TypeSafe AI released Jev, its first System One model. Founder Diogo Almeida previously worked at OpenAI on the instruction-following research behind ChatGPT.
Jev does not chat, write code or summarize. It takes unstructured state and returns typed decisions with calibrated probabilities. That makes it a natural fit for the thousands of small judgments inside an agent loop: which model to call, whether a command is safe, which passage is relevant, whether the agent is actually done.
How Jev Works
Every call sends a state (text or JSON) plus a dictionary of typed questions. TypeSafe’s docs define 3 primitives:
- Choice picks one option from a list and returns a probability per option plus confidence.
- Score rates the state on ordered rubric levels and returns probabilities plus confidence.
- Noul returns the probability (0 to 1) that a statement is true.
All questions are evaluated in parallel against the same state in one request. TypeSafe trains Jev with Reinforcement Learning for Calibrated Decisions (RLCD), so higher confidence should track higher accuracy. Choice supports up to 255 options.
The main claims, 193.6x faster and 444.6x cheaper, come from TypeSafe’s own workflow evals. The launch post says these figures sit on the higher end of real-world gains and use GPT-6 Astra and Fable 5.1 as the reference answer.