OpenAI Structured Outputs vs Decision Models

OpenAI's Structured Outputs make a model's response match a JSON schema you supply, so your code gets valid, parseable fields instead of free text. Decision models like Jev, from TypeSafe AI, solve a narrower problem differently: they answer a closed question with a probability for every allowed choice. Both give you machine-readable output, but they're not substitutes, and the difference shows up exactly where pipelines break.

For the general concept of structured output across providers, including framework integrations such as LangChain's, see structured outputs for LLMs.

What OpenAI Structured Outputs do

You provide a JSON schema, and the model's output is constrained to conform to it: required fields present, types correct, enums respected. That removes a whole class of failures: malformed JSON, missing keys, a "maybe" where your code expected true or false.

What the schema guarantees is shape, not correctness. A schema can force "category": "billing" to be one of your allowed values. It can't make the model pick the right one, or tell you how sure it was.

Structured Outputs shine when you need several fields at once, including generated text: extract the vendor, the total, the dates, and write a one-line summary, all in one validated object.

What decision models do differently

A decision model takes a closed question and a set of choices and returns a probability for each, per ecosystem documentation of the Choice API (probability outputs explained). There's no free text to constrain, because the model doesn't generate any.

The distribution is the point. "Billing: 0.51, account: 0.47" and "billing: 0.98" are the same answer from a schema's point of view, and completely different situations operationally. The first should go to a person. With probabilities, that routing is a threshold rule, not a guess.

Side by side

When to use each, and when to use both

Use Structured Outputs when you need generated content in a reliable shape: extraction, summaries with fields, tool arguments.

Use a decision model when the output is a choice and you need to know how confident it is: routing, moderation, verification gates, triage.

Use both in one pipeline, and this is the common production pattern: a generative model extracts fields into a schema, and a decision model checks them. "Does the extracted total match the line items?" "Is this document legible enough to trust?" That's the verifier shape in the decision model versus LLM comparison, and it's how intelligent document processing pipelines stay honest.

Frequently asked questions

What are OpenAI Structured Outputs?

A feature that constrains model responses to match a JSON schema you supply, so outputs are valid and parseable. OpenAI's docs note the exceptions to handle: safety refusals come back in a separate refusal field, and a response cut off at the token limit can be incomplete.

Do Structured Outputs make answers more accurate?

They guarantee format, not correctness. The model can still pick a wrong value that fits the schema.

Can a decision model return JSON?

Its output is a set of probabilities per choice, which your code can store however it likes. It doesn't generate free-form fields or text.

Which is cheaper for classification?

Decision models are priced for single-answer judgments, and builder receipts show classification costing fractions of a cent per item. Check current pricing for any provider you use.

Should I replace Structured Outputs with a decision model?

Not for extraction or generation. Add a decision model where you need confident choices and a routing rule for uncertain ones. See what Jev is.