A 0.6B decision model on Qwen3-0.6B-Base from the DocsGPT team. Give it a state and typed questions (yes/no, a choice of up to 16 options, a 3- or 4-level score) and it returns calibrated probabilities in one pass, for RAG and agent checks.
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
choice, score, noul, classify, rank, route
choice, noul, route, classify
Architecture
arc-decide
decisiontune
Fine-tuned from
qwen/qwen3-0.6b-base
answerdotai/modernbert-large
License
mit
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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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 arc-decide and decisiontune?
arc-decide is from Arc53 and decisiontune from DecisionTune. Both have open weights you can download and run. Both answer choice, noul, classify and route questions. Only arc-decide answers score and rank. decisiontune is the smaller model, at 395M parameters to 600M. arc-decide is licensed mit; decisiontune, apache-2.0.
Which is more accurate, arc-decide or decisiontune?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, arc-decide or decisiontune?
arc-decide: Free (open weights). decisiontune: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run arc-decide or decisiontune locally?
Yes, both: systemone pull arc53/arc-decide and systemone pull decision-tune/decisiontune download the weights.
