# How is a System One model different from an LLM with structured outputs?

An LLM with structured outputs generates a value that's guaranteed to fit your schema, while a System One model returns a probability for every answer you allowed, in one pass, trained so those probabilities track how often it's right. Both give you a valid value, and neither makes that value correct. That was a recurring objection in the [Hacker News thread](https://news.ycombinator.com/item?id=49717558) on Jev's launch, and TypeSafe's own [launch post](https://typesafe.ai/blog/introducing-system-one-models-and-jev) calls the 0% hallucination rate it plots for Jev "not empirical": it follows from guaranteed schema matching, which rules out an invalid value but not a [wrong answer that looks right](/answers/hallucinations/why-does-a-wrong-answer-look-confident).

The practical differences are the probability, the speed, and the price. An LLM does assign probabilities to its tokens, but TypeSafe's [AI primer](https://docs.typesafe.ai/introduction/machine-learning-primer.md) argues RLHF trains it toward answers people prefer, not probabilities that match accuracy. The launch post puts Jev at 70 to 500 milliseconds per call against 3 to 329 seconds for frontier LLMs, and $0.042 per million input tokens with output free, all TypeSafe's own figures.

What you give up is the reasoning. A System One model can't explain an answer, so a decision that needs a justification attached still belongs on an LLM.

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Sources:
- TypeSafe AI, "Introducing System One Models & Jev" (September 15, 2026): https://typesafe.ai/blog/introducing-system-one-models-and-jev (fetched 2026-09-21)
- Hacker News discussion, "Introducing System One Models and Jev": https://news.ycombinator.com/item?id=49717558 (fetched 2026-09-21)
- TypeSafe docs, AI primer (RLHF vs RLCD, calibration): https://docs.typesafe.ai/introduction/machine-learning-primer.md (fetched 2026-09-21)

Source: https://tessary.ai/answers/system-one-models/system-one-model-vs-llm-structured-outputs
More on System one models: https://tessary.ai/answers/system-one-models
From Tessary, agent reliability for AI agents in production: https://tessary.ai