Comparison
OpenAI Evals vs LLMJury
OpenAI Evals scores a model against a dataset you assembled. LLMJury scores a change against the traffic your users actually send.
OpenAI Evals is an open-source framework and hosted product for evaluating model outputs against datasets, run from the CLI or the OpenAI platform. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What OpenAI Evals is good at
- It is where most teams start, and that is not an accident: if you are already calling OpenAI, evals are one API away and there is nothing new to adopt.
- The dataset-and-grader model is the right shape for regression testing. Freeze the cases that matter, run them on every change, and a model upgrade stops being a leap of faith.
- The hosted version puts eval runs next to your logs, so building a dataset out of real requests is a short path rather than a project.
- Open source, so a grader you write is a grader you can read, version, and run anywhere.
Where LLMJury does something else
An eval answers "how does this do on the cases in my dataset?". That question is bounded by the dataset, and a dataset is a sample somebody chose — usually the cases that were interesting enough to save. The distribution your users bring is wider, messier, and the place a prompt change quietly costs you something.
LLMJury asks the other question. Real users are split deterministically between versions, a sample of the outputs they actually received is graded, and latency, cost, and business outcomes are measured on the same population at the same time. Nothing is compared across two different weeks of traffic.
The other half of the difference is what happens to the number. An eval gives you two scores and leaves the interpretation to you. LLMJury gates the result on a chi-squared SRM check — if the split broke, no verdict is shown at all — and corrects every metric-by-variant comparison for false discovery before reporting significance. Two eval scores that differ by three points may or may not mean anything; the whole point of the online experiment is that the answer to that is computed rather than eyeballed.
Side by side
| Dimension | LLMJury | OpenAI Evals |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | Evaluating model and prompt outputs against a dataset you assemble |
| Core unit | The online experiment — a variant split running on real users | The eval run — a dataset scored by a grader |
| Where the inputs come from | Live traffic, in whatever distribution your users produce | A dataset you chose and curated |
| Quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | Graders — string checks, model-graded rubrics, custom code |
| Statistical treatment | Chi-squared SRM hard gate (analysis halts, no result shown), per-metric test routing, Benjamini–Hochberg FDR correction across every comparison, raw and corrected p-values both reported | Scores per dataset; significance is left to you |
| Prompt management | Prompts live outside your repo with version history, audit trail, diff, and one-click rollback — and the version you edit is the variant that gets tested | Prompts live in your repo or in their platform, per your setup |
| Assignment | Deterministic local MurmurHash3 bucketing, identical in Python, TypeScript, and Java — no network call on the request path | Not applicable — no split |
| Provider coverage | Any provider; wrap() intercepts OpenAI-shaped and Anthropic-shaped clients | Built around the OpenAI platform |
Written from each product’s own published positioning, and last checked against it on 8 August 2026. Both products move — if something here is out of date or unfair, tell us at [email protected] and we will correct it.
Questions people ask on this page
- Can I use OpenAI Evals and LLMJury together?
- Yes, and the sequence is the useful part: the eval gates the deploy, the experiment decides the rollout. They measure different populations — your curated dataset and your live traffic — so neither result substitutes for the other.
- Do I have to move my datasets or graders?
- No. LLMJury does not ingest datasets and does not run offline evals. Its judge metrics are defined separately as a natural-language rubric plus an output schema, and the rubric library has nine written out in full. Nothing you have built offline needs to change.
Should you run both?
Run both, in that order. The eval catches what you already know can break, before anything ships; the experiment tells you whether the change helped the people you did not have a test case for. A team that only runs evals ships changes that pass every case and lose on conversion. A team that only runs experiments spends live traffic discovering something a fixed dataset would have caught in ten seconds.