An open Jev-style decision model by Andy Zhang: a LoRA adapter on Gemma 4 26B-A4B (also on Gemma 4 12B) that reads a state and returns a temperature-calibrated probability for every option of a yes/no, choice or score question in one forward pass.
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
choice, score, noul, classify, route
Architecture
ajev
laya
Fine-tuned from
google/gemma-4-26b-a4b-it
answerdotai/modernbert-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
—
—
Calibration error
—
—
Valid action rate
—
—
Median latency
49 ms
39.5 ms
p95 latency
—
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 ajev and laya?
ajev is from Andy Zhang and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, score and noul questions. Only laya answers classify and route. laya is the smaller model, at 421M parameters to 26B.
Which is more accurate, ajev or laya?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, ajev or laya?
ajev: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, ajev or laya?
By their publishers’ figures, laya answers in about 39.5 ms at the median and ajev in about 49 ms — measured on different hardware, so treat it as a rough guide.
Can I run ajev or laya locally?
Yes, both: systemone pull andy-zhang/ajev and systemone pull convai-innovations/laya download the weights.
