DecisionTune's 395M decision model: ModernBERT-large fine-tuned with a 4 KB scoring head that scores a marker per option, answering a choice or yes/no question in one encoder pass with a probability for every option. Runs locally on CPU or GPU via PyTorch, MLX or ONNX.
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, route, classify
choice, score, noul, classify, route
Architecture
decisiontune
laya
Fine-tuned from
answerdotai/modernbert-large
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
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Calibration error
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Valid action rate
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Median latency
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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 decisiontune and laya?
decisiontune is from DecisionTune and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, noul, route and classify questions. Only laya answers score. decisiontune reads up to 8K tokens of state, against 512 tokens for laya. decisiontune is the smaller model, at 395M parameters to 421M.
Which is more accurate, decisiontune or laya?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, decisiontune or laya?
decisiontune: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or laya locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull convai-innovations/laya download the weights.
