An open-weights System One model from Convai Innovations. A fully fine-tuned ModernBERT-large encoder with a from-scratch decision head that scores one marker per option and answers every question in a single 33–39 ms pass. Runs on your own CPU or GPU.
TokenRhythm's prefill-only decision model for agent workflows. Built on its NeoHorse-1-4B (a Qwen3.5-4B derivative), it predicts Choice, Noul and Score answers over application-defined options without generating text, with optional single-image input.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
laya
neohorse
Fine-tuned from
answerdotai/modernbert-large
tokenrhythm/neohorse-1-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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75.3%
Calibration error
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Valid action rate
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Median latency
39.5 ms
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p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between laya and neohorse-jev?
laya is from Convai Innovations and neohorse-jev from TokenRhythm. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. laya is the smaller model, at 421M parameters to 4.0B.
Which is more accurate, laya or neohorse-jev?
Only neohorse-jev publishes an accuracy figure (75.3% on JevBench public set (231 items), vLLM, maker's run); laya does not, so there is no comparison to make without your own test.
Which is cheaper, laya or neohorse-jev?
laya: Free (open weights). neohorse-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run laya or neohorse-jev locally?
Yes, both: systemone pull convai-innovations/laya and systemone pull tokenrhythm/neohorse-jev download the weights.
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Evaluation suite
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JevBench public set (231 items), vLLM, maker's run
