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.
Omar (kouhxp)'s CPU decision model: a Qwen3.5-0.8B fine-tune shipped as GGUF for llama.cpp that answers yes/no, choice and score questions with a probability per option plus a 'none of these' reject probability, served by a local Jev-style HTTP runtime.
Decides
choice, noul, route, classify
choice, score, noul, classify, route
Architecture
decisiontune
gutsy
Fine-tuned from
answerdotai/modernbert-large
qwen/qwen3.5-0.8b
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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73.2%
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 decisiontune and gutsy?
decisiontune is from DecisionTune and gutsy from Omar (kouhxp). Both have open weights you can download and run. Both answer choice, noul, route and classify questions. Only gutsy answers score. decisiontune is the smaller model, at 395M parameters to 800M.
Which is more accurate, decisiontune or gutsy?
Only gutsy publishes an accuracy figure (73.2% on JevBench public set (231 items; 169 correct), the maker's own run with Q8_0); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or gutsy?
decisiontune: Free (open weights). gutsy: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or gutsy locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull kouhxp/gutsy download the weights.
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Evaluation suite
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JevBench public set (231 items; 169 correct), the maker's own run with Q8_0
