Decision models

A decision model answers typed questions about an input and returns probabilities, never prose. You ask "which team owns this ticket?", "is it urgent?" or "how risky is it, 0–2?", and get a calibrated number for every allowed answer. Your code acts on the number. TypeSafe's Jev started the category; Cloudflare's Clef followed with a compatible API.

#How it works

One request carries a state (the input) and named questions. All of them are answered against the same state. There are three question types:

• choice picks from 1–255 named options and returns the top one plus a probability for each.
• score places the input on 1–10 ordered levels and returns the probability-weighted position. A score of 1.4 means it sits between levels 1 and 2.
• noul is a yes/no question and returns the probability of "yes".

{
  "model": "jev-latest",
  "state": "Charged twice for October. Need it fixed before payroll runs Friday.",
  "questions": {
    "team":   { "type": "choice", "instructions": "Which team owns this ticket?",
                "criteria": { "billing": "Payments, invoices, refunds", "bug": "Something is broken", "sales": "Plans and pricing" } },
    "urgent": { "type": "noul", "instructions": "Does the customer need a reply today?" },
    "churn":  { "type": "score", "instructions": "How likely is this customer to leave?",
                "criteria": ["Unlikely", "Possible", "Likely"] }
  }
}

An abridged response, with illustrative values:

{
  "answers": {
    "team":   { "type": "choice", "choice": "billing", "confidence": 0.97, "probabilities": { "billing": 0.97, "bug": 0.02, "sales": 0.01 } },
    "urgent": { "type": "noul", "noul": 0.88 },
    "churn":  { "type": "score", "score": 1.4, "probabilities": { "0": 0.12, "1": 0.36, "2": 0.52 } }
  }
}

Because every answer is a probability, "send it to a human when unsure" becomes a threshold in your code, such as confidence < 0.7, rather than a hope written into a prompt.

#Jev and Clef

Cloudflare reports median latencies of about 39 ms for Clef-flash, 209 ms for Clef and 524 ms for Jev. In its own benchmarks, Clef wins on most tasks but trails Jev on a few, including When2Call (deciding whether to call a tool). Those are vendor numbers. Run your own evals before switching.

#When to use one

They fit bounded decisions in a hot path: routing a request to the right model or prompt, triage, moderation, and "should the agent stop and ask?" gates. Compared with a generative model plus structured outputs, they're faster and cheaper per call. They also return a distribution, not a single sampled label.

They don't write replies, extract free-text fields or reason through multi-step problems. Pair them with an LLM that does.

Two cautions. Probabilities are only as calibrated as your data allows, so pick thresholds from a labelled test set. And state is untrusted text: prompt injection can tilt the verdict. Keep a hard rule or a human on any gate that grants access or moves money.