explainer · updated 2026-09-21
What is Jev? The System One model, explained
Jev is an AI model from TypeSafe AI that decides instead of writing. Give it any text or JSON and a few typed questions: it answers all of them at once, with a probability for every possible answer. It is the first System One model.
- maker
- TypeSafe AI
- launched
- 2026-09-15
- answers
- choice · score · noul
- price
- $0.042 / M input tokens
- median latency
- 0.44–0.48 s
- model
- jev-1.13.0
What is a System One model?
An AI model that answers typed questions with probabilities, all in one pass, and never writes text.
System 1 · fast
One look, one answer
Jev: every question answered at once, as probabilities.
System 2 · slow
Step by step, in words
LLMs: text one token at a time, reasoning by writing.
The name borrows the fast, intuitive System 1 of Daniel Kahneman's Thinking, Fast and Slow. Most decisions software makes are that kind: spam or not, which team, safe to run.
typed
Answers only with your options. Nothing to parse.
parallel
Every question in one pass. Ten take as long as one.
probabilistic
Odds for every answer, not just a pick.
cheap
No text is generated, so output is free.
How does Jev work?
State in, decisions out. One API request carries the input and every question, and each question is answered on its own.
- state
- What the decision is about: any text or JSON. The context window is about 32,000 tokens, text only.
- question
- A typed ask,
choice,scoreornoul, with an instruction. - criteria
- The possible answers, described in plain words.
- independence
- Questions never see each other's answers. Combine them in code.
What are choice, score and noul?
Jev's three question types: pick one option, place on an ordered scale, or answer yes or no.
choice → match
Pick one of up to 255 options.
request state + question
- message
- Can I move my table from Friday to Saturday night?
What does the customer want?
choicebookchangecancelaskwith descriptions
score → sort
Place it on 2 to 10 ordered levels.
request state + question
- review
- Food was fine and the service slow, but the view made up for it.
How positive is the review?
scoreNegativeMixedPositiveGlowing
noul → if
Yes or no, as the probability of yes.
request state + question
- Your parcel is held at customs. Pay the $2.99 release fee within 24 hours: parcel-release.help/pay
Is this a phishing attempt?
noulyesno
How is Jev different from ChatGPT and other LLMs?
An LLM writes its answer token by token, and your code parses it. Jev is not an LLM: it writes nothing and scores your answers, all at once.
LLM · one token at a time
Jev · every answer at once
| LLMs | Jev | |
|---|---|---|
| Output | Text to parse | Probabilities over your answers |
| Writes text or code | Yes | No |
| Reasons step by step | Yes | No: one look per question |
| Latency (median) | 0.53–2.21 s | 0.44–0.48 s |
| Cost per 1,000 decisions | $0.023–$1.5 | $0.029–$0.043 |
| Possible answers | Anything, including made-up ones | Only the options you defined |
| Probabilities | Only if asked to write them | Built in, trained to be calibrated |
| Best at | Open-ended work | Many fast decisions over known answers |
Measured on the Jevals boards: Jev and 6 LLMs, release 2026-09-18.
Is Jev just a classifier?
Close: it does zero-shot classification, and critics say so. The difference from a trained classifier: you describe the options in words and change them on any request.
| Trained classifier | LLM with structured outputs | Jev | |
|---|---|---|---|
| Training data | Needed | None | None |
| New answers | Retrain | Edit the prompt | Edit the request |
| Output | Scores per label | Text that should parse | Probabilities per answer |
| Speed and cost | Fastest | Slowest | Fast |
What do Jev's probabilities mean?
They are meant to be calibrated: answers given at 80% should be right 80% of the time. That lets code act alone when Jev is sure, and ask for help when it isn't.
sure: act
unsure: ask
yes/no
gap 5.0 pts
pick-one
gap 9.8 pts
rubric scores
gap 19.7 pts
What is Jev used for?
Decisions with known answers that software makes often: route, check, rank, choose.
- 1 · propose
Code or an LLM lists the options.
- 2 · decide
Jev scores them against the state.
- 3 · gate
Code checks the confidence.
- 4 · act
Sure: do it. Unsure: escalate.
choice
Routing
Send each ticket, email or prompt to the right place.
noul
Guardrails
Approve an agent's command, or ask a human.
choice · noul
Moderation
Publish, redact or reject.
score
Judges and linters
Grade outputs against a rubric, in place of an LLM-as-a-judge.
score
Ranking and reranking
Score every search result, then sort.
choice
Classification
Label a million rows by topic, intent or sentiment.
choice
Agents
Pick the next action or the element to click.
choice
Real-time loops
Game bots deciding several times a second.
choice
Extraction
Code finds candidates, Jev picks one or none.
Limitations: when not to use it
- Writing anything: replies, summaries, code.
- Answers you can't list in advance.
- Arithmetic or multi-step reasoning.
- Security boundaries: text in the state can sway it.
- Its documented weak spots: counting, dates, negation.
How does Jev score on benchmarks?
Tied for first on yes/no and tied for second on pick-one, for a fraction of most LLMs' cost. On rubric scores, no model clearly beats guessing.
| Board | Test | Jev Decision Score | Jev accuracy | Best other model |
|---|---|---|---|---|
noul | PubMedQA | 69.0 | 91.3% | Gemini 3.8 Flash 73.0 |
choice | Banking77 | 67.8 | 79.7% | Gemini 3.8 Flash 74.1 |
score | HelpSteer2 helpfulness | 9.2 | 41.3% | GLM-5.3 7.8 |
Scored against human labels, five runs per question; orange marks each board's top 2. Head to head: Jev vs Gemini 3.8 Flash.
Who makes Jev?
TypeSafe AI, a San Francisco startup led by CEO Diogo Almeida, a former OpenAI researcher on InstructGPT.
- TypeSafe AI founded, in stealth
- Talks on what comes after RLHF
- Jev launches
- On Vercel AI Gateway
- First independent benchmark
- training
- RLCD, Reinforcement Learning for Calibrated Decisions. Unpublished, like the architecture.
- name
- After the Jevons paradox: make something cheaper and use grows.
- access
- TypeSafe's API (waitlist), or Vercel AI Gateway as
typesafe-ai/jev. - funding
- $40M seed round led by DCVC.
What is a Jev-type model?
Any model that answers choice, score and noul with probabilities. Jev itself is closed; adapters and open-source alternatives now copy its interface.
| Model | What it is | Open | On Jevals |
|---|---|---|---|
| Jev | The native System One model | No | Listed |
| LLMs via an adapter | An LLM prompted for the same probabilities (system-one-adapter, AI SDK evaluate) | Varies | 6 listed |
| openjev | Option probabilities read from an open Qwen model | MIT | Coming |
| decider-2b | A 2B Qwen model fine-tuned for the three types | Apache-2.0 | Coming |
| DiffusionGemma | Google's diffusion model filling an answer template (open-jev) | Open weights | Not yet |
| GLiClass, GLiNER | Small zero-shot encoder classifiers, the prior art | Apache-2.0 | GLiClass coming |
What are the criticisms of Jev?
Mostly fair: it is close to a zero-shot classifier, and most claims about it are the vendor's own.
- "Just a zero-shot classifier." Partly true: encoders like GLiClass came first. New is one hosted model, three types, calibration as a goal.
- "Can't hallucinate." It can't invent an answer, but it can pick the wrong one, confidently.
- Vendor benchmarks. TypeSafe's Workflow Evals grade agreement with two LLMs, not human labels.
- A black box. No paper, no parameter count, no published calibration.
- The price. Critics suspect a subsidy; TypeSafe says it is profitable.
Facts checked 2026-09-21 against TypeSafe's and Vercel's public pages. Jevals is independent of TypeSafe AI.