A walkthrough of Jev, a new "system one" classification model from Typesafe (built by a co-inventor of ChatGPT) that only returns true/false answers, a pick from a menu, or a 0-10 score — which is why it's roughly 20-200x faster and 40-400x cheaper than general-purpose LLMs, with free output tokens and ~4 cents per million input tokens. The pitch is to pair it with a system-two model like Claude rather than replace one. Three concrete uses are demoed: automated model routing inside Claude Code (70% token savings across 12 test prompts), high-volume business classification like lead triage, and semantic search in apps.
- Jev is not an LLM; it only outputs classifications in three shapes — binary true/false, a selection from a menu of options, or a score on a scale — which is the reason it's so fast and cheap.
- Pricing is inverted from normal models: output tokens are free and only input is charged at about four cents per million tokens, making it roughly 24x cheaper than Haiku and 230x cheaper than the top-tier model the creator compares it to.
- Typesafe frames Jev as the first "system one" model (fast, snap decisions) versus existing "system two" models that reason word by word — the recommended play is combining both, not choosing.
- Access was just opened to everyone via Typesafe's API, and the speaker personally connected through OpenRouter; hooking it into Claude Code takes a single setup prompt.
- Model routing use case: in a 12-prompt comparison, letting Jev choose which Claude model handles each task produced about 70% savings because nine of the twelve tasks never needed the most expensive model.
- Skill routing use case: across 14 tests searching a workspace of ~145 skills, Jev surfaced the right skill in about 5 seconds total versus roughly 30 seconds when the top model did the selection.
- For business automation, the pattern to look for is high volume plus a business question — lead qualification from 100 emails, support ticket triage, invoice fraud detection, spam filtering, community moderation, refund requests, and churn classification.
- App-level uses include semantic image/video search (matching by meaning instead of filename) and a Chrome extension called Unclutter that uses Jev to classify and strip ads, cookie banners, and other page slop.
This is the core mechanic that explains every claim in the video — the pricing model and the deliberate output constraint.
Its output tokens, basically its response to you as the user, is always free, and they only charge for the input tokens... Jev can only answer in three shapes... a binary response whether that statement is true or false, a selection against a menu of options, or a response that is based on scale.
The most concrete measured result in the video, plus the practical tip to build an on/off skill command before committing to it.
I asked Claude to do a comparison of around 12 prompts with Jev and another one where it's running with the top model every time... it actually resulted to 70% savings because nine out of those 12 tasks never needed the top model anyway.
Jev is a new type of frontier AI model that is 20 to 200 times faster and 40 to 400 times cheaper.
If there's one takeaway from all of this, the best way you can use Jev right now is to combine both — a system one model like Jev with a system two model like the Claude models.
What are the things that you are receiving in volume that you need to classify?