System One scorecard
The scorecard is the Metatext suite: three workflows, seven questions, 123 judgments, plus an eight-case extraction task. It is small, so a ±0.1 gap is a tie, but it is the one place every checkpoint we have ever run appears on one table, including the GLiClass payload variants.
| # | Model | Decisions | Beats baseline | ECE raw → held-out fitted | p50 / case | Extraction F1 |
|---|---|---|---|---|---|---|
| 1 | JevTypeSafehosted | 0.863 | 6/7 | 0.093 | 441 ms† | n/a |
| 2 | GLiNER2 largeFastino | 0.716 | 6/7 | 0.261 | 627 ms† | 0.968 |
| 3 | GLiNER 2.5 baseFastino | 0.643 | 5/7 | 0.251 | 332 ms† | 0.968 |
| 4 | GLiClass large v3.0Knowledgator correct payload, no prompt | 0.642 | 5/7 | 0.250 | 977 ms | n/a |
| 5 | GLiNER2 baseFastino | 0.631 | 3/7 | 0.350 | 190 ms† | 0.935 |
| 6 | GLiClass multilang ultraKnowledgator criteria + single-hypothesis Noul (correct payload) | 0.628 | 4/7 | 0.185 | 672 ms | n/a |
| 7 | GLiClass base v3.0Knowledgator criteria + single-hypothesis Noul (correct payload) | 0.606 | 5/7 | 0.146 | 300 ms | n/a |
| 8 | GLiClass modern large v3.0Knowledgator criteria + single-hypothesis Noul (correct payload) | 0.602 | 4/7 | 0.234 | 572 ms | n/a |
| 9 | GLiNER 2.5 smallFastino | 0.574 | 4/7 | 0.172 | 132 ms† | 0.812 |
| 10 | GLiClass instruct base v1.0Knowledgator criteria + single-hypothesis Noul (correct payload) | 0.559 | 3/7 | 0.390 | 323 ms | n/a |
| 11 | GLiClass instruct large v1.0Knowledgator criteria + single-hypothesis Noul (correct payload) | 0.545 | 3/7 | 0.308 | 1.0 s | n/a |
| 12 | GLiGuard 300MFastino | 0.537 | 3/7 | 0.230 | 155 ms† | 0.000 |
| 13 | LAYAConvai Innovations | 0.520 | 3/7 | 0.064 | 378 ms† | n/a |
| 14 | Vonwfzyx | 0.509 | 4/7 | 0.234→ 0.122 | 597 ms | n/a |
| 15 | GLiNER 2.5 multiFastino | 0.494 | 4/7 | 0.244 | 346 ms† | 0.885 |
| 16 | LAYA typed-decisionsConvai Innovations | 0.475 | 3/7 | 0.131 | 375 ms† | n/a |
| 17 | GLiNER2 multiFastino | 0.464 | 2/7 | 0.354 | 202 ms† | 1.000 |
| 18 | LAYA multilingualConvai Innovations | 0.431 | 2/7 | 0.284 | 149 ms† | n/a |
| 19 | OpenJEVopen-jev | 0.427 | 3/7 | 0.210 | 50 s† | n/a |
| 20 | GLiClass edge v3.0Knowledgator criteria + single-hypothesis Noul (correct payload) | 0.418 | 2/7 | 0.287 | 41 ms | n/a |
| 21 | GLiNER multitask v1.0Knowledgator class names as labels | 0.378 | 1/7 | 0.223 | 1.8 s | n/a |
Ranked by decision accuracy. Metatext decisions: Three hand-written workflows (support tickets, security incidents, agent traces), seven questions, about 123 judgments per model. Small, so treat a ±0.1 difference as a tie; every question has a majority baseline the model has to beat. Every model is zero-shot on this suite. Decisions is the task-weighted mean accuracy of the best configuration per model; ECE is the mean expected calibration error over tasks, and the arrow shows the held-out ECE after one temperature per primitive fitted on the other half. Latency is p50 per three-question customer-service case on four CPU cores (Jev: API round trip); † means taken from the benchmark run rather than a clean timing pass. Updated September 2026.
Same ranking on the other suite: typed-decisions.
Frequently asked questions
Why two suites?
The Metatext suite is hand-written and small; it was the first exam and it is the only one with an extraction task. typed-decisions is public and twenty times larger, so it carries the ranking. A model that wins one and not the other is worth a second look.
What does "beats baseline" mean?
For every question the majority class is a free baseline. A model that does not beat it on a question has learned nothing useful about that question. The column counts questions won.
Related
The benchmark for System One models: decision models that answer typed questions with calibrated probabilities. Jev and every open alternative on the same 2,000-decision suite, with accuracy, wins over the majority baseline, ECE and latency.
An independent benchmark of TypeSafe's Jev against every open System One alternative: decision accuracy, calibration (ECE) and latency on two suites, 2,123 judgments, same harness for every row.
Micro-F1 on an eight-case entity extraction task for the models that have a span head. Jev, LAYA, Von and GLiClass never return spans; only the GLiNER family does.
System One models ranked by p50 latency per three-question case on a four-core CPU, against Jev's hosted round trip.