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learnjev

FAQ

Short answers

The questions that come up most, answered in a paragraph each. Where the honest answer is “nobody knows yet”, it says that.

The basics

What is Jev?
TypeSafe AI's first System One model, released 15 September 2026. You send it a blob of state and a map of typed questions; it returns typed answers with calibrated probabilities. It generates no text at all. TypeSafe's own framing: "a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out."
Who makes it?
TypeSafe AI, founded 2024, out of stealth on 15 September 2026 with a $40M seed round led by DCVC. Founders: Diogo Almeida (CEO, ex-OpenAI, InstructGPT), Erik Gafni (CTO) and Sasha Sheng (COO, ex-Meta/FAIR). Headquartered in San Francisco.
Where does the name come from?
"System One" is from Kahneman's Thinking, Fast and Slow — fast intuitive judgment versus slow deliberate reasoning. "Jev" is after William Stanley Jevons, whose paradox holds that efficiency gains increase total consumption. The bet is that cheaper intelligence means far more of it gets used.
What does “Noul” stand for?
Nothing that has been published. TypeSafe's docs never expand it. It is the name of the yes/no primitive and that is all anyone outside the company knows.

Access and cost

Can I use it today?
Yes, and you no longer need the waitlist. TypeSafe's own early access at console.typesafe.ai is still gated, but within three days of launch Jev shipped on OpenRouter, Vercel AI Gateway (typesafe-ai/jev) and Cloudflare AI Gateway (typesafe/jev — note that is AI Gateway, not Workers AI, which does not carry it). Those are the fast routes to an experiment.
What does it cost?
$0.042 per million input tokens. Output tokens are reported in usage but are not charged. There is no published free tier, trial credit or plan structure — typesafe.ai/pricing returns a 404, and the Models page is the only pricing artefact that exists.
Is the pricing sustainable?
Unknown, and TypeSafe says so: they concede they cannot prove it is not subsidised, and that they expect it to go down rather than up. Treat a design whose economics only work at $0.042/MTok as carrying that risk.
Is there prompt caching?
No. A TypeSafe engineer confirmed on Hacker News: "Nope, it's always the same input token cost." If you send the same large state repeatedly, you pay for it every time — which is an argument for asking all your questions in one request rather than several.
Where does it run, and is there a zero-data-retention option?
US servers only at launch; no EU region has been announced. Zero data retention is an enterprise-tier option, not a default. Both were named by practitioners as adoption blockers in week one.
What are the rate limits?
250,000 tokens per second and 1,200 requests per minute for jev-1.13.0; exceeding either returns 429. TypeSafe warns explicitly that these "can change without notice" while they add capacity.

Capabilities

Does it really never hallucinate?
It never returns a value outside the type you declared — that is structural and genuinely useful. It can absolutely return the wrong valid value. The 0% on TypeSafe's own hallucination chart is, in their footnote, "not empirical": it is added by definition. The accurate claim is never makes type errors.
Can it handle images or audio?
No. Text only — a string, a JSON object, or an array of text values. TypeSafe's docs add "(yet…)", which suggests intent rather than a roadmap.
How many options can a Choice have?
Up to 255. Above a certain cardinality TypeSafe's sampler runs a two-stage process — score independently, then pick — which they note causes occasional slowdowns. A Score takes between 2 and 10 levels.
How many questions can I send at once?
There is no documented cap. The only ceiling is the token budget — 64k for the whole request, 32k for the state plus the longest single question. TypeSafe's own cookbooks routinely run 180–220 questions in one call.
Does it explain its answers?
No, and none is promised. The probability distribution is the only diagnostic you get, which is a strong reason to log probabilities rather than just the summary value. For decisions that need a written rationale, the usual approach is to escalate flagged or low-confidence cases to a generative model.
Is it deterministic?
Apparently not quite. A developer measured identical calls varying by up to ±0.04, while still separating their cases cleanly (>0.95 on segments with an error, ≤0.10 on those without). Another reported seeing no variance. It is unresolved. Either way, threshold with margin rather than testing a probability for exact equality.
Can I fine-tune it?
No. TypeSafe states the same weights serve every account, and there is no fine-tuning API. Questions are defined per request instead — which is the point, and the main thing that distinguishes it from a trained classifier.

Judgement calls

Is this just a classifier?
This is the most common reaction, and the founder agreed with it — asked directly on Hacker News whether it is basically a zero-shot classifier, he replied "exactly right!". The "it's just a BERT" framing dominates r/LocalLLaMA, with the popular guess being that TypeSafe runs prefill only and reads the fields in parallel. That guess is unverified; no architecture has been published. Two things are genuinely different from a fine-tuned classifier: questions are defined at runtime with no labelled data and no training step, and calibration is trained for rather than bolted on. If you already have labels and a stable task, an encoder fine-tune is probably still cheaper, faster, self-hosted and auditable.
Why not just use JSON mode on the model I already have?
For many tasks you should. Constrained decoding already solves output validity. What it does not give you is a calibrated confidence — and that is the one claim here that structured outputs genuinely cannot match. If neither latency nor calibration is in your critical path, the switch is an optimisation looking for a reason.
Are the speed and cost claims real?
The cost claims hold up; the speed claims are quoted from the top of the vendor's own range. The homepage's 193.6× / 444.6× is one best-case demo that TypeSafe itself flags as "on the higher end". Independent testers measured roughly 3–11× against fast models, and one clocked Jev at p50 176ms against a 27B model on Cerebras hardware at 215ms. Cheapness is the durable advantage; raw speed is not a moat.
Has anyone verified the calibration?
Partly, and the answer is split. TypeSafe has published nothing. A pre-registered community study across 8,576 responses found Jev calibrated on one dataset (ECE 0.0204) and systematically overconfident on another (ECE 0.0936). Another found that on rows where Jev reported confidence ≥0.9 it was only 72.2% accurate. Calibration appears to be dataset-dependent, so measure it on yours.
Is it safe to put in a control path?
With care. TypeSafe documents that state is not treated as hostile by default, so prompt injection through user-supplied state is live. Keep permissions, limits and rollback in code, give every question an honest "none of the above" option, and never let one Jev answer be the last thing before an irreversible action.

Longer versions of most of these live in Concepts, and the full API surface is on the reference page.