No eval to write, no ML vocabulary, no leaderboard guesswork. You describe the job; we measure every candidate on your data and name the cheapest one that’s good enough — then keep it honest as the market moves.
Write what you want the model to do in plain English, and add a handful of your real, labelled examples — the inputs you see and the answers you’d accept. No test harness, no prompt-engineering rabbit hole. If you don’t have examples handy, we’ll help you generate a representative set.
You bring: a sentence + a few examplesModeljury auto-constructs a fair evaluation for your task, then scores a curated roster of models — cloud APIs and open / self-hosted alike — head-to-head on your data. Grading is programmatic against your labelled answers, so there’s no expensive, biased LLM-judge in the loop. Compliance and residency rules prune candidates before cost is even considered.
We do: a real, apples-to-apples testYou get one clear answer — the cheapest model that clears your quality bar — backed by the evidence: an accuracy-vs-cost report, a confidence range on the result, and the runners-up so you can see exactly what you’re trading off. A verdict you can put in front of your team and your finance lead.
You get: the cheapest model that’s good enoughNew models ship every week. We re-run your evaluation as they land and alert you the moment a cheaper one clears your bar — so you never quietly overpay on last quarter’s pick. The one-off verdict is useful; the ongoing watch is the product.
Always on: alerts when a cheaper model winsEvery step is built so the answer is evidence, not endorsement. We don’t make or sell a model, the grade is a number measured on your own data, and the same method runs across every vendor — US or not, cloud or open. Cheaper-and-good-enough wins, whoever built it.
A jury weighs the evidence — it doesn’t ask one of the defendants who should win.
Type a task, watch the bake-off, get the verdict. The first run is free.
Try it now — free →