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.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, noul, route, classify
choice, score, noul, classify, route
Architecture
decisiontune
mira
Fine-tuned from
answerdotai/modernbert-large
jhu-clsp/mmbert-small
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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61.3%
Calibration error
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0.083
Valid action rate
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100.0%
Median latency
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30 ms
p95 latency
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43 ms
Evaluation suite
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s1-decision-bench
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 mira?
decisiontune is from DecisionTune and mira from SAGEA. Both have open weights you can download and run. Both answer choice, noul, route and classify questions. Only mira answers score.
Which is more accurate, decisiontune or mira?
Only mira publishes an accuracy figure (61.3% on s1-decision-bench); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or mira?
decisiontune: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or mira locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull sagea/mira download the weights.
