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Home - TypeSafe AI

Source: https://typesafe.ai/

We took the opposite research direction

not chat

Reinforcement Learning from Human Feedback (RLHF) has led to LLMs that are optimized for human preferences. This has led to models that are superhuman at instruction following, and are what we now call “chat.” Yet RLHF creates inherent issues such as mode dropping, overconfidence, and lack of reliability. These flaws mean that LLMs require humans-in-the-loop.

a new model

We built a new class of models, System One Models, to be natively used by machines. We’re building with a new architecture, a new sampler, and a new training algorithm: Reinforcement Learning for Calibrated Decisions (RLCD).

Decisions, not strings

Typed outputs that software can act on.

calibrated confidence

Every decision includes an estimate of how confident the model is.

more like code

Reliable, fast, and type-safe.

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193.6x Faster, 444.6x Cheaper.

*based on workflows for System One tasks(proof)

TypeSafe AI

Cost $0.000081

Completed in 0.114s

LLMs

Cost $0.013880

Completed in 8.566s

Built for automation

Jev returns typed decisions with calibrated probabilities, so your software can account for uncertainty. Set the thresholds for when it acts autonomously and when it asks for review. Combine those decisions in code to build larger workflows, with control over how the intelligence is used.

Jev’s intelligence per dollar is literally off the charts.

Machine-Native Intelligence

LLMs produce words for people. Jev produces typed decisions and is more like code: reliable, fast, self-consistent, and type-safe.

Zero Hallucinations

Every Jev decision comes with a confidence estimate, so your software can act when confidence is high and escalate when it is not.

$42

Per Billion input tokens.

238x

Lower input price than Claude Fable 5.1

Come Build With Us

We give a FAQ

What are System One Models? What is Jev?

System One Models are a new class of AI model built for decisions inside software. Jev is TypeSafe’s first public System One Model, optimized for automation. Send Jev structured questions and get typed decisions with probabilities and confidence that your software can act on.

Is Jev just a smaller LLM?

How is this different from JSON mode or structured outputs?

How can Jev be so fast and inexpensive?

Can you make Jev even faster?

Are these prices temporary or subsidized?

What is Jev good at? Where does it struggle?

Can Jev still get things wrong?

How do I get started or ask a question?

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