Describe your task in plain English. We run a real bake-off across every model, name the cheapest one that's good enough, then keep watching as new ones ship.
The same engine answers both questions teams actually have — which hosted model, and whether to self-host at all. Nobody else compares both on your real task.
Claude · GPT · Gemini · hosted open models. Connect via API, zero ops, the latest frontier models — and we monitor every launch for a cheaper one that clears your bar.
Self-hosted / on-premise open models — often cheaper at scale, and your data never leaves. Compliance and residency rules prune the candidates before cost even matters.
High-volume, objectively-gradable jobs — the boring-but-expensive backbone tasks every team runs thousands of times a day.
The first answer is free or cheap. The monitoring — keeping you on the best-value model as the market moves — is the subscription, and where the value sits.
Free on your own keys, or $5 on ours. Then $9 / test pay-as-you-go. No commitment.
Continuous re-testing, alerts when a cheaper model clears your bar, API access. Solo $29 · Team $199.
Ask ChatGPT which LLM is best for your task and you get a confident, conflicted guess. Models carry training and commercial bias — an OpenAI model won’t talk up Grok, and a US-trained model rarely champions a Chinese one like DeepSeek (the reverse holds too). And it’s answering from general reputation, not your task: it hasn’t tested anything on your data, doesn’t know the cheaper model that shipped last week, and never measures the accuracy-vs-cost tradeoff that actually decides it.
A jury weighs the evidence — it doesn’t ask one of the defendants who should win. Modeljury is the jury, not a contestant.
Because a model can’t impartially judge models. Its pick is shaped by training and commercial bias — an OpenAI model won’t champion Grok; US-trained models rarely recommend Chinese ones like DeepSeek, and vice-versa. It also answers from general reputation rather than your task: it hasn’t tested anything on your data, doesn’t know the cheaper model released last week, and never measures accuracy against cost. Modeljury doesn’t ask for an opinion — it runs a real bake-off on your examples and grades the results. Evidence, not endorsement.
You can run a first bake-off in an afternoon. The hard part is everything after: an evaluation tailored to your task, grading without a costly LLM-judge, a catalog kept current as models ship weekly, compliance and self-hosting, and continuous re-testing so you stay on the cheapest model that still clears your bar. That ongoing watch — not the one-off script — is the product.
It’s usually the most expensive, not the best fit. For classification and extraction, a small open model often clears your bar at a fraction of the price. “Best in the world” isn’t the question; “cheapest that’s good enough for this task” is — and only a measurement on your own data can answer it.
We don’t make or sell a model. Grading is programmatic against your labelled examples, so the verdict is a number — and the exact same method runs across cloud and open, US and non-US models alike. Cheaper-and-good-enough wins, whoever built it.
Run on your own API keys or fully self-hosted, so your data never leaves your environment. Residency and compliance filters prune the candidates before cost is even considered.
That’s exactly why monitoring is the core product, not the one-off verdict. We re-run your evaluation as new models launch and alert you the moment a cheaper one clears your bar.
The cheapest model that clears the bar — and a watch that keeps you there.
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