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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.

Leads on decision accuracy: Jev — 0.863 on Metatext decisions
#ModelDecisionsBeats baselineECE raw → held-out fittedp50 / caseExtraction F1
1JevTypeSafehosted0.8636/70.093441 ms†n/a
2GLiNER2 largeFastino0.7166/70.261627 ms†0.968
3GLiNER 2.5 baseFastino0.6435/70.251332 ms†0.968
4GLiClass large v3.0Knowledgator
correct payload, no prompt
0.6425/70.250977 msn/a
5GLiNER2 baseFastino0.6313/70.350190 ms†0.935
6GLiClass multilang ultraKnowledgator
criteria + single-hypothesis Noul (correct payload)
0.6284/70.185672 msn/a
7GLiClass base v3.0Knowledgator
criteria + single-hypothesis Noul (correct payload)
0.6065/70.146300 msn/a
8GLiClass modern large v3.0Knowledgator
criteria + single-hypothesis Noul (correct payload)
0.6024/70.234572 msn/a
9GLiNER 2.5 smallFastino0.5744/70.172132 ms†0.812
10GLiClass instruct base v1.0Knowledgator
criteria + single-hypothesis Noul (correct payload)
0.5593/70.390323 msn/a
11GLiClass instruct large v1.0Knowledgator
criteria + single-hypothesis Noul (correct payload)
0.5453/70.3081.0 sn/a
12GLiGuard 300MFastino0.5373/70.230155 ms†0.000
13LAYAConvai Innovations0.5203/70.064378 ms†n/a
14Vonwfzyx0.5094/70.234→ 0.122597 msn/a
15GLiNER 2.5 multiFastino0.4944/70.244346 ms†0.885
16LAYA typed-decisionsConvai Innovations0.4753/70.131375 ms†n/a
17GLiNER2 multiFastino0.4642/70.354202 ms†1.000
18LAYA multilingualConvai Innovations0.4312/70.284149 ms†n/a
19OpenJEVopen-jev0.4273/70.21050 s†n/a
20GLiClass edge v3.0Knowledgator
criteria + single-hypothesis Noul (correct payload)
0.4182/70.28741 msn/a
21GLiNER multitask v1.0Knowledgator
class names as labels
0.3781/70.2231.8 sn/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.

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