A multimodal decision classifier over text, image, audio and video on Gemma 4 12B, fine-tuned on 30,000 questions. Returns a probability per option and generates nothing.
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, noul, score, classify
choice, score, noul, classify, route
Architecture
jev-omni
laya
Fine-tuned from
google/gemma-4-12b-it
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
87.6%
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Calibration error
0.040
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Valid action rate
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Median latency
83 ms
39.5 ms
p95 latency
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Evaluation suite
DecisionBench Medium (author's set)
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 jev-omni and laya?
jev-omni is from akhilaaa3 and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, noul, score and classify questions. Only laya answers route. laya is the smaller model, at 421M parameters to 12B.
Which is more accurate, jev-omni or laya?
Only jev-omni publishes an accuracy figure (87.6% on DecisionBench Medium (author's set)); laya does not, so there is no comparison to make without your own test.
Which is cheaper, jev-omni or laya?
jev-omni: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, jev-omni or laya?
By their publishers’ figures, laya answers in about 39.5 ms at the median and jev-omni in about 83 ms — measured on different hardware, so treat it as a rough guide.
Can I run jev-omni or laya locally?
Yes, both: systemone pull akhilaaa3/jev-omni and systemone pull convai-innovations/laya download the weights.
