What is a System One model?
A System One model returns typed, calibrated decisions instead of text. What that means, where the name comes from, and how Jev fits.
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Guides to System One models and Jev
Ten explainers, in reading order: what a System One model is, what Jev returns, how it differs from a language model, and what the vendor's numbers do and do not prove. Every factual claim carries a source.
All guides
Jev explained: TypeSafe AI's decision model
Jev is TypeSafe AI's System One model: it returns typed decisions and calibrated probabilities, not text. Specs, pricing, access and limits.
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System One models vs LLMs
System One models return typed decisions; LLMs return text. A side-by-side on output, latency, cost, hallucination and when each one wins.
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Choice, Score and Noul: the three primitives
Choice, Score and Noul are the three question types a System One model answers. Concrete examples, their limits, and how confidence is derived.
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How to build with System One models
State in, typed questions out. Code owns the thresholds and side effects. The four official patterns, and the jaggedness list as design constraints.
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RLCD explained: training a model to be calibrated
RLCD is TypeSafe's training method for Jev: Reinforcement Learning for Calibrated Decisions. How it differs from RLHF and RLVR, and what is public.
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Is Jev just a zero-shot classifier?
Engineers called Jev a zero-shot classifier within hours of launch. What the comparison gets right, what it misses, and what is actually new.
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Jev speed and pricing
Jev costs $0.042 per million input tokens with free output. TypeSafe claims 70ms to 500ms and 40x to 200x. What those numbers cover.
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How to get Jev API access
How to get Jev API access: the TypeSafe waitlist, the console key page, the TYPESAFE_API_KEY env var, SDK installs, and a first working call.
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Jev architecture: what is known and what is not
What TypeSafe has said about Jev's architecture: parallel output in one pass, typed answers, RLCD. What it has not said: weights, parameters, design.
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