Decision models and structured responses
Jev vs OpenAI Structured Outputs
Both return data your application can read. Jev is built around focused decisions over shared state; OpenAI Structured Outputs constrains a model response to a JSON Schema. The right choice depends on the job your code needs done.
This compares Jev's decision API with the Responses API's structured text output, not every OpenAI model or tool. No speed, price, or accuracy winner is assumed.
At a glance
Different contracts for different jobs
A schema defines the shape of an answer. A decision API also defines the kinds of questions and signals returned for each one.
Primary task
Classify, score, or make a yes/no judgment that application code can act on.
Generate a model response that follows a supplied JSON Schema, for extraction, generation, classification, and other tasks.
Output contract
Choice, Score, and Noul have defined answer shapes tied to their question types.
You define the supported JSON Schema in text.format with strict mode; the response follows that schema when successfully completed.
One request
Send one state and several named questions, each answered under its question ID.
Define one response schema. It can contain multiple fields or nested objects for several related results.
Uncertainty
Choice and Score can return option probabilities and confidence; Noul returns a yes probability.
Schema adherence does not by itself provide calibrated probabilities or establish that a judgment is correct. Refusals and incomplete responses need handling.
Best fit
Repeated, well-scoped decisions such as routing, urgency, and human-review gates.
Flexible structured generation when the application needs a custom object, explanation, extraction, or broader model workflow.
Same ticket, two request shapes
What you send to each API
For a billing ticket, Jev describes the decisions as typed questions. The OpenAI request describes the desired response object as a strict schema.
Jev: state + typed questions
Choice selects a team; Noul asks whether a person should review the case.
{
"model": "jev-latest",
"state": "I was charged twice and need a refund.",
"questions": {
"team": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": "Payments and refunds",
"technical": "Product issues",
"other": "None of the above"
}
},
"needs_review": {
"type": "noul",
"instructions": "Does this need human review?"
}
}
}OpenAI: input + JSON Schema
The schema constrains the returned team and review flag to known fields and types.
{
"model": "gpt-4o-mini",
"input": [
{ "role": "developer", "content": "Classify this support request." },
{ "role": "user", "content": "I was charged twice and need a refund." }
],
"text": {
"format": {
"type": "json_schema",
"name": "ticket_triage",
"strict": true,
"schema": {
"type": "object",
"properties": {
"team": {
"type": "string",
"enum": ["billing", "technical", "other"]
},
"needs_review": { "type": "boolean" }
},
"required": ["team", "needs_review"],
"additionalProperties": false
}
}
}
}These are illustrative JSON request bodies. Send them to their respective APIs with server-side credentials; validate results and apply your own business rules.
Which should you use?
Choose Jev for focused decisions
Use it when your application already knows the allowed choices or scoring rubric and benefits from per-option decision signals.
Choose Structured Outputs for custom data
Use it when you need a tailored JSON object, extracted fields, or structured generated content within an OpenAI workflow.
Combine them when useful
An application can use structured generation to prepare information, then ask Jev a narrow question before taking an action.
For consequential actions, evaluate both approaches on your own cases and keep deterministic policy checks, sensible thresholds, and human review where needed.
Sources
Product and API behavior above is based on the Jev AI GitHub README and the official OpenAI documentation for Structured Outputs in the Responses API.