Jev vs Asking an LLM for Structured Output
This is what almost everyone is doing today, and it works. A schema, a constrained decoder, a validated object. The comparison is not "does it work" — it is what the remaining failure modes cost you once you are running millions of them.
Three things survive the schema: you do not learn how sure the model was, you pay for the tokens it spent thinking, and the numbers it emits are not calibrated just because they are typed.
Background
What Structured Output Actually Guarantees
Modern constrained decoding is good. Given a JSON schema, the major providers will reliably give you an object that validates against it, with the enum member you asked for and not a synonym. That solved the parsing problem, and it solved it properly — anyone still writing regexes to pull JSON out of prose should go and use it.
What it does not do is make the content of the object mean more than it did. A field called "confidence" with an enum of high, medium and low is a generated token, not a probability, and it does not sum to anything across the alternatives. When you want to route the uncertain 5% to a human, that field will not tell you which 5% they are.
And it is priced as generation. You are billed for the reasoning tokens the model produced to arrive at the object, which at scale is the majority of the bill for a task whose useful output is one label.
Side by side
What Actually Differs
Both approaches read the same text. This is what differs after that.
| Jev (via jev-ai.org) | LLM + JSON schema | |
|---|---|---|
| Output validity | Guaranteed by the answer shape | Guaranteed by constrained decoding |
| Probability per alternative | Yes, for every label and every tier | No — a self-reported confidence field at best |
| Calibration | Trained for; you should still verify on your data | Not a property the schema provides |
| Output tokens | Counted, never billed | Billed, and often the larger half of the bill |
| Adding a fourth criterion | A few dozen extra input tokens on the same call | More output tokens, and often a longer chain of reasoning |
| Same task, also writes prose | Never | Yes — which is why you use it for everything else |
| Latency | No tokens to stream; upstream P50 ~0.22s | Scales with how much the model writes |
How to choose
Neither One Wins Every Time
Choose a decision model when
- The output is a branch in your code, not something a person reads.
- You need to know which cases the model was unsure about, so you can route those somewhere else.
- The volume is large enough that output-token billing is a line item you have opinions about.
- The decision is on a hot path and a streamed answer is latency you cannot spend.
- You want several judgements about one piece of text without paying to read it several times.
Stay with structured output when
- The task needs extraction as well as judgement — pull the fields and also draft the reply.
- You do not know the schema in advance, or it varies per document.
- The reasoning itself is the product: you want the explanation shown to a user or an auditor.
- Volume is small. At a thousand calls a month the economics do not matter and one fewer vendor does.
- You need world knowledge the decision model does not have — this is a judgement about the text you send, not a lookup.
We sell one of these two, so read the right-hand column with that in mind — and then run both on two hundred of your own labelled examples, which settles it better than any page on the internet can.
Questions
Jev vs LLM Structured Output: Common Questions
Can I keep my existing LLM and just add this for the hard part?
That is the most common sensible architecture. Use the decision model as the gate — route, filter, screen, score — and call the large model only on what gets through. Most pipelines find the majority of their requests never needed the large model at all.
Is not "confidence: 0.9" in the JSON good enough?
It is a token the model generated because the schema asked for a number. It is not derived from the distribution over alternatives, it does not sum to one across them, and it tends to be overconfident and clustered on round numbers. If you plan to threshold it, check it against labelled data first — most people who do are surprised.
What about logprobs from the chat API?
Better, and genuinely useful if your label is a single token. It gets awkward fast with multi-token labels, with any reasoning before the answer, and with providers that do not expose them. It is the same idea as a decision model, done by hand and with less support.
Do I lose the ability to ask "why"?
You lose the generated explanation, which was never an audit trail anyway — it is a plausible story written after the fact. What you gain is the distribution, which is a real account of what the model considered. When you need a written rationale for a human, ask a chat model for one on the cases that matter.
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Other Comparisons
The Cheapest Way to Settle It
Load a scenario, paste in your own text, and see what the distribution says. Editing costs nothing and needs no account.