Jev vs LLM: When to Use a Decision Model Instead of Generating Text

Say you’ve got a product that’s getting 40,000 support tickets a month, and every one of them needs to be triaged to give three things: what it is about, should it be escalated, and what the reply is. A frontier LLM with a JSON schema can do the job. Our benchmark run shows it doing so at a cost of $2.88 per 1,000 tickets and a two-second median, or about $115 a month. A small LLM did it for $0.09 per 1,000 at about one second. Another way of thinking about it is to ask what a decision model can do. Jev is one from TypeSafe and, in the same run, it did the triage for two and a half cents per 1,000 tickets at a median of 194 milliseconds, or about a dollar a month. Plus it returns probabilities, so there is nothing to parse. It can’t write the reply though. For that, you still need an LLM. So we benchmarked Jev, GPT Luna, and Claude Opus on 100 support cases through OpenRouter and built two patterns that make use of both. Ask a language model to make a code decision and it returns text. You can ask…