An open family of System One models. A LoRA adapter plus a pointer head on a frozen Qwen base returns a distribution per typed question in one forward pass, serves TypeSafe's /v1/systemone contract, and ships a fitted temperature with every checkpoint.
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.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
kev
laya
Fine-tuned from
qwen/qwen3.5-4b-base
answerdotai/modernbert-large
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
83.8%
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Calibration error
0.042
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Valid action rate
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Median latency
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39.5 ms
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 kev and laya?
kev is from Jared Palmer and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. kev reads up to 8K tokens of state, against 512 tokens for laya. laya is the smaller model, at 421M parameters to 4.0B.
Which is more accurate, kev or laya?
Only kev publishes an accuracy figure (83.8% on transfer-v4 (locked, out of domain)); laya does not, so there is no comparison to make without your own test.
Which is cheaper, kev or laya?
kev: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run kev or laya locally?
Yes, both: systemone pull jared-palmer/kev and systemone pull convai-innovations/laya download the weights.
