Models
September 19, 2026 · View on GitHub
All System One models share POST /v1/systemone. The model field selects which one.
Current (fetched 2026-09-19)
| Versioned ID | jev-1.13.0 |
| Aliases | jev-latest → jev-1.13.0; jev-preview → same (no preview build right now) |
| Price | $42 / Btok input = $0.042 / Mtok. Output tokens free. |
| Rate limits | 250k tokens/s and 1,200 rpm (dynamic; 429 over either). Higher on enterprise. |
| Context | 64k tokens/request; 32k for state + longest question |
| Input | Text only. String, object, or array of text. |
Pin jev-1.13.0 if you have tuned thresholds against that version; aliases move on release. Response model reports the ID that answered.
Jev is not fine-tuned per account. Shape behavior with state, instructions, and criteria. Not trained on customer requests. English is the primary training language.
evaluate default: jev-latest (TypeSafe) or ~typesafe/jev-latest (OpenRouter).
Official: models
Jaggedness (jev-1.13, reviewed 2026-09-17)
| Failure | Do this |
|---|---|
| Literal reading | Write the exact condition; put boundaries in criteria |
| Math, counting, numeric encodings (hex, RGB) | Compute in code; ask one noul per item and sum |
| Date/time order, duration, windows | Extract parts as choice (include "not stated"); compare in code |
| Indirection / double negatives | Direct instructions; name the relevant state paths |
| Large irrelevant state | Filter first; optional noul for relevance |
| Adversarial / injected instructions | Precise criteria; test edges |
| Contradictory instructions vs criteria | Align them |
Expected identities (P + P(not) = 1, noul vs yes/no choice) | Ask each decision one way; enforce identities in code |
| Generation / free-form extraction | Candidate in code or a generative model; Jev selects |
Score interpolation between levels is weakly calibrated — threshold, don't reconstruct a magnitude.
Avoid: asking what code can compute; hiding several judgments in one question; System Two multi-hop reasoning; stuffing unused context.
Official: jaggedness
Training (why not an LLM)
Jev is trained with RLCD (reinforcement learning for calibrated decisions): typed answers and probabilities, not preferred prose (RLHF) and not long chain-of-thought (RLVR). Calibration is a group property (P=0.8 should be right ~80% of the time), not a guarantee on one answer.
TypeSafe's claim on System One tasks vs LLMs: orders of magnitude faster/cheaper; no string generation. Treat marketing multiples as theirs, not ours.