Nate Herk tests Jev, a new decision-only model from TypeSafe AI (trained with RLCD, reinforcement learning for calibrated decisions), across 12 use cases. Jev doesn't write text — it only outputs yes/no calls, category picks, and scores with confidence levels — which makes it dramatically faster and cheaper than chat models for classification work. In his benchmark, 1,000 emails across seven classification rules took Jev 6 seconds and 9 cents parallelized, versus 5 minutes and 62 cents for a frontier chat model. He argues the real play is using Jev as a cheap front-end filter in automations and then handing the narrowed-down data to a smarter model for actual analysis.
- Jev only makes three kinds of decisions — a yes/no (called a 'null'), a pick-one category ('choice'), and a numeric score — each returned with a confidence level in JSON; it cannot write, summarize, or find themes.
- The announced claims are 20-200x faster and 40-400x cheaper than chat models, with output tokens free; the main limitation is a 64,000-token input context window versus roughly a million for models like Claude or GPT.
- In the email test, 1,000 emails scored on seven rules cost 9 cents and took 70 seconds unparallelized, 6 seconds after the backend was tuned for parallel bigger payloads — compared to 5 minutes and 62 cents on a chat model.
- Across all his testing, his Jev console showed almost 20,000 requests for a total of 85 cents.
- The recommended pattern is a two-model pipeline: let Jev cheaply sort thousands of items (YouTube comments, support tickets, posts), then feed only the relevant subset to a reasoning model for replies or theme analysis.
- Jev doesn't analyze for you, but if you design the questions well the aggregate scores tell a story — e.g. spotting that meetings routinely end with no clearly owned next steps.
- He built a Chrome extension that labels X posts as breaking news, golden nuggets, or AI slop in real time, and a paper-trading bot that makes a buy/sell/hold decision every second for about $2 a day in model cost — though trading fees dwarf the model cost.
- Before swapping Jev into production, run evals against a golden dataset of ~100 known-correct answers and compare Jev to other models on accuracy, cost, and speed.
The concrete side-by-side benchmark, including the jump from 70 seconds to 6 seconds after parallelizing, is the clearest evidence for the whole cost-and-speed argument.
With Luna, that took 5 minutes, and it was 62 cents, compared to 70 seconds, and I think it was 9 cents. And also, think about it wasn't just doing one sort of classification, it was doing all seven of these rules.
The one piece of practical caution in the video: cheap and fast only counts if accuracy holds, and he gives a specific method for checking it.
Don't just plug in Jev and trust what it says automatically. What you're really going to want to do is run evals, meaning you're going to have a golden data set of 100 use cases and 100 correct answers.
The interesting thing about Jev is that it's an AI that makes decisions, but it doesn't write anything. It doesn't output tokens.
I've used 85 cents with Jev. But look how many requests I've done. I've done almost 20,000 requests.
Jev on its own doesn't analyze things for you. But if you're strategic with the way that you set up the questions, you can get analysis from it.