Jev is HERE. How to use it
Greg Isenberg and Ryan Vogel (founding team at OpenCode) break down "Jev," a new classifier-style AI that takes an input plus an output schema and returns probability-weighted decisions instead of generated text. The headline demo: classifying 1,700 emails by category, priority, spam score, and reply-worthiness for 18 cents, with roughly 200ms per query. They walk through where it fits (lead scoring, support routing, instant quotes, clip-finding, browser agents) and where it fails (a Bitcoin buy/hold/sell signal that performed badly), plus how to get access via the Vercel AI gateway while it's still waitlisted.
- Jev is a classifier, not a chat model: you give it an input and an output schema, and it returns probabilities across your defined options rather than generated text or reasoning traces.
- The email demo processed 1,700 emails (4.2M input tokens, 500K output tokens) across four classifications for 18 cents total, finishing almost instantly instead of the hours Ryan expected.
- Each query takes about 200 milliseconds regardless of input or output structure, so there's no streaming, no listeners, and no waiting — just a single fast API call.
- Because output is type-safe structured data (numbers come back as number objects, not text), developers can drop results straight into code with no post-processing.
- Ryan hacked it into a text generator by making A–Z the schema options so it picked letters one at a time — it worked, but proved the point that it isn't a conversational LLM.
- The startup framing Greg lands on: find a business with an expensive queue of incoming information and put Jev at the front of it — lead triage, support ticket routing, instant quotes for local services.
- It has real limits: a Bitcoin buy/hold/sell signal running every minute performed poorly, and Ryan warns not to put it in front of a portfolio or anything needing deep model intelligence.
- Other demos: scoring 17 clip-worthy moments from a video transcript in about 3 seconds, and the browser-use team using Jev to book a Zurich-to-London flight in 7.1 seconds.
- Access is waitlisted, but you can use it immediately through the Vercel AI gateway; Ryan suggests asking your own AI agent which of your daily workflows would benefit from a decision maker.
The single number that makes the case for Jev, delivered live with Ryan's own reaction to the speed and cost.
It's probably going to take like 10 hours to do... and oh, the price is going to be so expensive and — oh, it's done. Oh, it didn't — cost 18 cents.
This is the reusable business framing, followed immediately by the concrete 'instant quote that's actually instant' example for local services.
How do I find a business with an expensive queue of incoming information and then just put Jev at the front of that queue?
You don't really ask Jev, because Jev isn't a text model. If you look at the actual spec, it doesn't generate any text at all.
If you're looking at some data and thinking, 'hm, I have to make a decision on this,' you should probably think about adding Jev at that layer.
It takes around 200 milliseconds per query, no matter what the input output structure is.