$ cat wiki/models/jev.md

Jev

modelupdated 2026-09-17created 2026-09-17

structured-output decisions calibration routing inference-cost agents

Spec

Attribute	Value
Developer	TypeSafe AI
Released	2026-09-15
Announced	2026-09-15
Context window	unknown
Pricing	$0.042/M input · output free
License	unknown
Availability	early access off a waitlist at typesafe.ai
Three of these rows need their reading stated, because each is `unknown` or odd
for a different reason
(source):

Context window is unknown and not a number. One pass reports a request budget of roughly 32,000 tokens, described in TypeSafe's docs as about 150,000 English characters. No pass used the term "context window", and a request budget for a model that makes one parallel forward pass over a state is not obviously the same quantity. The figure is recorded here; the row stays unknown until something names it.
Pricing carries a free half. TypeSafe states input at $42 per billion tokens (= $0.042/M) and output free, its stated reason being that the architecture makes output too inexpensive to meter (3 passes). No Catalogue id row is present: Jev is in waitlisted early access and appears in no catalogue any pass returned, so spec-check will report it not-listed, which is the correct answer for this model rather than a defect.
License is unknown, not proprietary. Nothing read names a licence or terms of any kind.

Release Date

2026-09-15, announced together with the System One Models class in a post titled Introducing System One Models & Jev (2 passes). One pass dates the opening of early access 2026-09-16 — recorded in ## Conflicting Reports (source).

What it is — a model that cannot answer you in words

Jev does not generate text. The caller sends structured or natural-language state together with a schema; the model returns a typed value inside that schema — a choice, a score, or a probability with a stated confidence — in one parallel forward pass rather than token-by-token decoding (3 passes).

Property	Value	Passes
Stated jobs	decide, classify, route, score	3
Output shape	typed value + calibrated probability / confidence	3
Decoding	one parallel forward pass, no autoregressive generation	3
Schema guarantee	stated that the model cannot emit an output outside the caller's schema	2
End-to-end latency	70–500 ms	3
Training method	RLCD — Reinforcement Learning for Calibrated Decisions	2
Request budget	~32,000 tokens ≈ 150,000 English characters	1
RLCD is named, not specified. Its stated goal is calibration — a stated 70%
confidence should be correct about 70% of the time. One pass records that the
**reward function, architecture, training procedure and calibration methodology
are all undisclosed** (source).

Benchmarks

There are no independent evaluations of Jev at launch (2 passes). What exists is one vendor figure (source):

Measure	Value	Harness	Passes
Accuracy vs "the most expensive frontier models"	within 3 points	TypeSafe's own workflow evaluations	1
Cost per call vs the same	~4,000× less	TypeSafe's own	1
**No benchmark this wiki tracks — not Artificial Analysis, not DeepSWE, not
Terminal-Bench, not any public suite — has been run against this model in anything
read. The comparator models are not named. Calibration, the property RLCD is
stated to optimise, has no published number at all**, which is the single most
checkable claim on the page and the one with nothing under it.

Pricing

$0.042 per million input tokens; output free (3 passes). For contrast within this wiki, Fugu Max — the other 2026 release that sells a non-standard object as one model — lists $2/M input · $6/M output, and Claude Opus 5 $10/M input.

The comparison is not like-for-like and this page does not present it as one: Jev returns a value, not a completion, so a "call" here is not the same unit of work. The 4,000× cheaper per call figure above is TypeSafe's own attempt at a like-for-like unit and is unverified.

Use Cases

Stated by TypeSafe, and the list is the product boundary rather than a set of suggestions (3 passes) (source):

Routing a request to the next step in an automation workflow
Classifying unstructured state into a caller-defined enum
Scoring a candidate, a result, or a confidence
Gating — a yes/no decision with a calibrated probability attached

Anything requiring prose is out of scope by construction. The pitch summarised by one pass — "a frontier-intelligence function call" — is the honest description of the surface.

Compared To

Fugu Max and Fugu Ultra v2 — Sakana AI's orchestrators, the other 2026 entrants sold as "a model" while not being a single trained network. They move in the opposite direction: Fugu makes one endpoint out of many frontier models and charges frontier-adjacent prices; Jev removes the language model from the decision entirely and charges ~1/50th of Fugu Max's input rate. Both are answers to the same observation — that a lot of production traffic is paying frontier prices for non-frontier work.
"Small frontier LLMs" — the comparator TypeSafe's own headline names and never identifies. Until it does, every multiplier on this page is a ratio with one side missing.

Conflicting Reports

The speed and cost multipliers

TypeSafe publishes four different answers for the same two quantities across its own surfaces — 193.6×/444.6× (home page), 40–200× (blog), 20–200× / 40–400× (founder's thread), >100×/>200× (the AINews headline carried first-party in this repo's feed). The full table and its reading are on TypeSafe AI. No figure is adopted here (source).

The early-access date

Two passes give 2026-09-15 for the launch and the opening of early access; one pass states early access opened 2026-09-16. The Released and Announced rows carry 2026-09-15 as the better-corroborated reading, and the alternative is recorded rather than discarded (source).

Sources

Referenced by

Model Routing TypeSafe AI