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 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.
Decides
choice, noul, route, classify
choice, score, noul, classify, route
Architecture
decisiontune
kev
Fine-tuned from
answerdotai/modernbert-large
qwen/qwen3.5-4b-base
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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83.8%
Calibration error
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0.042
Valid action rate
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Median latency
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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 decisiontune and kev?
decisiontune is from DecisionTune and kev from Jared Palmer. Both have open weights you can download and run. Both answer choice, noul, route and classify questions. Only kev answers score. decisiontune is the smaller model, at 395M parameters to 4.0B.
Which is more accurate, decisiontune or kev?
Only kev publishes an accuracy figure (83.8% on transfer-v4 (locked, out of domain)); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or kev?
decisiontune: Free (open weights). kev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or kev locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull jared-palmer/kev download the weights.
