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.
TokenRhythm's prefill-only decision model for agent workflows. Built on its NeoHorse-1-4B (a Qwen3.5-4B derivative), it predicts Choice, Noul and Score answers over application-defined options without generating text, with optional single-image input.
Decides
choice, noul, route, classify
choice, score, noul, classify, route
Architecture
decisiontune
neohorse
Fine-tuned from
answerdotai/modernbert-large
tokenrhythm/neohorse-1-4b
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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75.3%
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 neohorse-jev?
decisiontune is from DecisionTune and neohorse-jev from TokenRhythm. Both have open weights you can download and run. Both answer choice, noul, route and classify questions. Only neohorse-jev answers score. decisiontune is the smaller model, at 395M parameters to 4.0B.
Which is more accurate, decisiontune or neohorse-jev?
Only neohorse-jev publishes an accuracy figure (75.3% on JevBench public set (231 items), vLLM, maker's run); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or neohorse-jev?
decisiontune: Free (open weights). neohorse-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or neohorse-jev locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull tokenrhythm/neohorse-jev download the weights.
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Evaluation suite
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JevBench public set (231 items), vLLM, maker's run
