Most developers reach for an LLM for almost everything: classification, routing, scoring, decision making.
It works.
But it is often the wrong tool.
In September 2026, TypeSafe AI released Jev, a model built only for decisions.
It does not generate text. It only decides.
Here is the real difference.
LLM: reads the prompt, writes the answer one token at a time, and leaves you to parse the text.
Jev: reads the state once, answers every question in a single parallel pass, and returns typed values with probabilities. Nothing to parse.
Key Numbers
| Metric | Jev | LLM |
|---|---|---|
| Latency | 70-500 ms | 3-30+ seconds |
| Cost | $0.042 / M tokens | $0.20-$10 / M + output |
| Output | Typed decision | Free-form text |
| Needs parsing? | No | Yes |
When to Use Which
- Jev when you need a decision: classify, route, score, yes/no
- LLM when you need generation: writing, reasoning, explanation
Most real systems should use both.
Bottom line:
Stop using a generation model for decision work.
Top comments (3)
The typed output distinction is useful, but I would separate returning a probability from demonstrating that the probability is calibrated for a particular routing task. A value like 0.8 only supports an automated decision threshold if similarly scored cases are correct at roughly that rate on the relevant data.
For a practical comparison, I would fix the label set, include ambiguous and out-of-scope inputs, and report error rate versus the fraction automatically handled at several thresholds. A constrained-output LLM would make a useful baseline too. That would show whether the benefit comes from decision quality, abstention behavior or the simpler output interface, alongside latency.
Agreed. Proving better performance requires task-specific benchmarks. That’s separate from the underlying difference in how the models work.
Agreed, those are separate questions. The typed interface can simplify integration even before there is evidence that the model makes better decisions on a particular dataset.
For an application comparison, I would hold the label definitions and downstream action policy fixed, then compare both raw predictions and decisions after thresholding. Otherwise a change in how the application handles uncertain cases can look like a model improvement. Reporting abstentions alongside incorrect decisions would also show whether the interface makes uncertainty usable in practice.