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Best open System One model

Ranked on the public typed-decisions suite, zero-shot, with LAYA's in-domain fine-tune shown for what a fine-tune buys.

Leads on decision accuracy: LAYA typed-decisions — 0.714 on typed-decisions
#ModelDecisionsBeats baselineECE raw → held-out fittedp50 / case
1LAYA typed-decisionsConvai Innovations
fine-tuned on this suite
0.714→ 0.76218/200.161→ 0.0163.5 s†
2GLiNER2 largeFastino0.4869/200.300→ 0.0293.8 s†
3Vonwfzyx0.4615/200.249→ 0.027597 ms
4GLiClass large v3.0Knowledgator
correct payload, no prompt
0.4467/200.315→ 0.093977 ms
5GLiNER 2.5 baseFastino0.3983/200.415→ 0.0591.3 s†
6GLiClass multilang ultraKnowledgator
correct payload, no prompt
0.3953/200.281→ 0.031672 ms
7LAYA multilingualConvai Innovations0.3452/200.322→ 0.0731.2 s†
8LAYAConvai Innovations0.3432/200.196→ 0.0203.2 s†

Ranked by decision accuracy. typed-decisions: LAYA's public benchmark: four workflows × 100 JSON states × five typed questions = 2,000 decisions. Synthetic; gold is a teacher distribution and the label is its argmax. Large enough to carry an ordering. Every model is zero-shot except LAYA typed-decisions, which was fine-tuned 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: Metatext decisions.

Jev-shaped models not yet run

Surveyed on Hugging Face; each takes the System One request. Their own published figures are on their model pages, attributed; none is comparable to the table above until it has a row.

  • Decider 2B1.9B · Apache 2.0

    A decoder-backbone System One reproduction with a proper-scoring-rule SFT stage and a calibration-aware RL stage; serves the /v1/systemone request shape.

  • open-jev DeBERTa-v3-large435M · Apache 2.0

    Single-pass encoder with a per-option head over state and all questions in one sequence; 512-token context with the state cut to 256 tokens.

  • Gavel base150M · MIT

    Von's mechanism trained deliberately for the decision task, small and multilingual, with a fitted temperature and the card's own negative results.

  • Kev 0.6B0.6B · Apache 2.0

    A reproduction of the architecture described in "Jev's Architecture Unmasked"; the only one that measured the real Jev on its own frozen dev sets and published a research log.

  • Jev schema scorer435M · Apache 2.0

    Scores the literal Jev question JSON as the second sequence, so criteria are read at inference.

  • System One Qwen3.5 4B scorer4.2B · Non-commercial

    Pointwise scorer trained on 46k System One questions across nine families; reports its calibration separately from its accuracy.

  • Qwen-Jeff B21.7B · Apache 2.0

    Pointwise scorer with a CLI and an "automate if p ≥ 0.9588" gate calibrated to at most 5% error.

  • RLCD ModernBERT 151M151M · Apache 2.0

    A GLiClass-mechanism model with an RLCD-style objective; small enough to run in the browser (ONNX + WebGPU demo).

Frequently asked questions

What is the best open System One model right now?

Zero-shot: GLiNER2 large. With a fine-tune on the target decisions: LAYA. Neither is a drop-in for Jev without one of those qualifiers.

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