Jev made 973 decisions in one agent run with zero output tokens; the same task cost $22.18 on a reasoning model before and $0.05 after, with the frontier model never called. The loop: prompt → structured state → Jev picks the model → worker runs.
Jev made 973 decisions in one agent run and generated 0 output tokens doing it same run, same output: $22.18 on a reasoning model before, $0.05 after, and the frontier model was never asked once the loop: prompt → structured state → Jev picks the model → worker runs → Jev
Gregor Zunic showcases an open-source browser agent combining Browser Use and Jev, finding flights in 7 seconds for $0.0039, using a new action space per step, DOM state space, and small LLM fallback for typing.
Scott Williams predicts custom Jev-style models (parallel constrained decoding) will make existing company agent systems more token efficient, creating millionaires.
The user praises the JEV model for its extremely fast reproduction of each movement and command, believing bots will become smarter as a result.
