Why TypeSafe's System One model is named Jev, and early benchmark results
Pete Soder explains TypeSafe's System One concept and the Jev name, and rounds up cost and accuracy data from MotherDuck, LangChain, Every and others using Jev for classification, scoring and judging.
.@typesafeai coined "System One models" on September 15. The name comes from Daniel Kahneman's 'Thinking, Fast and Slow'. Fast thinking is System One. Quick calls like reading the mood on a face. Slow is System Two. Thoughtful work like multiplying 17 by 24 in your head. In TypeSafe's framing, LLMs are System Two. They write every answer one token at a time, and reasoning models spend extra tokens thinking before they commit. A System One model skips the slow parts. You send it some text (like a support ticket) plus questions whose answers are fixed in advance: question: "Which team owns this?" options: ["billing", "technical", "sales", "account"] question: "Does the customer want a refund?" options: ["yes", "no"] It sends back a pick for each question with a probability attached, something like "billing, 0.94" and "refund, 0.95." Because every possible answer is known before the model starts, it can score them all in one pass and answer in well under a second. TypeSafe named Jev after the Jevons paradox. When something gets cheaper to use, people end up using far more of it. Here's a very small fraction of what's been published so far by teams testing TypeSafe's first System One Model: ➔ @MotherDuck ran it over 100,000 labeled news articles. Jev got 89% right in 40 seconds for $0.50. The most accurate LLM they tested got 88% in 32 minutes for $37.58. ➔ @LangChain used it to grade an agent's work. It matched the expected verdict on all 500 decisions for $0.34 total, against $28.17 for Claude Sonnet 4.6. ➔ @Every had it judge writing in a median 0.35 seconds per passage, against 8.83 seconds for Fable 5.1. It caught 6 of 7 planted defects; Fable caught all 7. ➔ @archestra_ai ran it on 100 tool calls from Claude Code sessions. It flagged 7 of the 9 dangerous ones to Sonnet 5's 4, (though Sonnet scored higher overall). When a decision costs a fraction of a cent and comes back in half a second, you can stop rationing it. Checks that used to run on a sample can run on every row and every agent step.