Berget AI's System One model for gating agent commands: a LoRA adapter and a fine-tuned joint schema head on Cloudflare's Clef-Flash that answer noul, choice and score questions over a state in one forward pass. Trained on Swedish and English operations decisions.
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.
Decides
noul, choice, score
choice, noul, route, classify
Architecture
clef
decisiontune
Fine-tuned from
cloudflare/clef-flash
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
97.0%
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Calibration error
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Valid action rate
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Median latency
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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 bev and decisiontune?
bev is from Berget AI and decisiontune from DecisionTune. Both have open weights you can download and run. Both answer noul and choice questions. Only bev answers score. Only decisiontune answers route and classify. decisiontune is the smaller model, at 395M parameters to 9.0B.
Which is more accurate, bev or decisiontune?
Only bev publishes an accuracy figure (97.0% on Berget held-out risk split (16,902 questions; same operations-traffic corpora as training)); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, bev or decisiontune?
bev: Free (open weights). decisiontune: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bev or decisiontune locally?
Yes, both: systemone pull berget-ai/bev and systemone pull decision-tune/decisiontune download the weights.
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Evaluation suite
Berget held-out risk split (16,902 questions; same operations-traffic corpora as training)
