Jev Cost

Halv reports a paired SWE-rebench comparison where its workflow, an Astra coordinator, a Luna investigation stage, and JEV selecting worker models and reasoning effort, recorded $144.67 in model cost versus $337.50 for vanilla Codex across 42 runs, with both arms passing 25 verifier runs.

Joe Maddalone on using Jev for routing and filtering before a generative model, with demo code in a transcript-analyzer repo.

ZAUEY on a model that picks from options you define and returns a probability, and why many questions on one state stay cheap.

A natural-language smart-room demo: describe a mood and Jev decides lights, curtains, TV, music, volume, and desk lamp in one request.

Cole Medin's week of hands-on testing, covering where Jev gates replace regex or slow LLM calls, plus demos that play his indie game and triage every open pull request.

codebasics explains why a non-generative model matters and walks through hands-on code, including the caveat that Jev cannot write text or code.

A head-to-head benchmark that prices 200 products from descriptions with Jev against GPT-5.6 Luna and GPT-4.1-Nano.

An internal benchmark report: Jev zero-shot came within about 5 points of recall of a fine-tuned model at matched precision, at roughly $70 a month.

A two-stage extraction cascade: a mini model extracts, a Noul battery verifies each field in one request, and a 0.7 gate escalates to a reasoning model, sitting on the cost/quality frontier.