Jev Sucks, Actually
A review of "Jev," a new AI model from the co-inventor of ChatGPT that is claimed to be 20-200x faster and 40-400x cheaper than frontier models, with free output tokens. The key point: Jev isn't an LLM at all, it's a classifier that labels, routes and scores inputs with confidence probabilities, and it can't reply, write code, explain itself, see media, or do math. After walking through use cases (email sorting, YouTube comment tagging, a Skittles-sorting demo, model/skill routing, Smash Bros, a self-driving simulation), the verdict is that Jev is currently overhyped for most users, though real-time decision-making at scale looks genuinely promising.
- Jev came out of a tweet by Diego Almeida, a co-inventor of ChatGPT, who spent two years asking why superhuman chat models haven't produced AGI and built Jev as an answer.
- The headline claims are 20-200x faster and 40-400x cheaper than frontier models, 4 cents per million input tokens, free output tokens, 24x cheaper than Haiku and 238x cheaper than the model called Fable in the video.
- Jev is a classifier, not a language model: it takes an input and returns probability-weighted options from a predefined schema (e.g. 80% orange, 10% red, 10% blue) rather than generating text.
- LLMs are built for human-in-the-loop conversation; Jev is built for computers and agents to talk to each other without a person involved.
- Jev can label, route and score things fast and cheaply, but it cannot reply, write code, explain its choices, process images/video/audio, or do math, counting or dates.
- The main practical weakness is the schema: you have to define every option in advance, so unanticipated edge cases (a black iPhone when the choices are blue/orange/red/green/yellow) get misclassified.
- The most useful everyday application shown is routing: using Jev to instantly pick which model or which skill should handle a given task, saving time and tokens.
- The strongest demos involve millions of split-second decisions — playing Smash-style melee games and powering a Tesla-like self-driving simulation — while email sorting and Skittles sorting mostly just showcase speed.
- The conclusion is that most people building with AI should stick with Anthropic or OpenAI models for now, since Jev is early, overrated and doesn't do anything on its own.
This is the video's central original criticism: the more edge cases exist, the harder it becomes for a human to author a workable schema.
What if his iPhone was black or white or gray, right? It doesn't appear in this schema. And so Jev is not going to know how to classify it, or it's just going to get it wrong.
After dismissing most demos, this is the one use case the reviewer calls genuinely compelling — real-time decisioning at millions of choices per second.
He's using Jev to power all of the decision making. So how it fits between the lines, what it does when it approaches the stop sign, how it handles pedestrians... this to me is where Jev becomes really, really powerful.
Jev is a classifier at its truest being — that's what it is.
LLMs are designed for human-in-the-loop interaction. Jev was basically built for computers and agents to talk amongst themselves.
Being able to classify things in and of itself is maybe not all that helpful. But what do you do with it next? I think is almost the bigger question.