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.
French medical decision models by Bofeng Huang: pass a patient message or clinical note, a question and candidate answers, and get one probability per answer from a single forward pass. Choice, score and noul. A research model, not a medical device.
Decides
choice, score, noul, classify, rank, route
choice, score, noul
Architecture
arc-decide
docto-decision
Fine-tuned from
qwen/qwen3-0.6b-base
qwen/qwen3.5-4b
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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88.9%
Calibration error
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0.079
Valid action rate
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Median latency
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47 ms
p95 latency
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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 docto-decision?
arc-decide is from Arc53 and docto-decision from Bofeng Huang. Both have open weights you can download and run. Both answer choice, score and noul questions. Only arc-decide answers classify, rank and route. arc-decide is the smaller model, at 600M parameters to 4.7B. arc-decide is licensed mit; docto-decision, apache-2.0.
Which is more accurate, arc-decide or docto-decision?
Only docto-decision publishes an accuracy figure (88.9% on Docto Decision Bench fr v0.1 (12 tasks, mostly silver labels; the maker's own benchmark)); arc-decide does not, so there is no comparison to make without your own test.
Which is cheaper, arc-decide or docto-decision?
arc-decide: Free (open weights). docto-decision: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run arc-decide or docto-decision locally?
Yes, both: systemone pull arc53/arc-decide and systemone pull bofeng-huang/docto-decision download the weights.
Evaluation suite
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Docto Decision Bench fr v0.1 (12 tasks, mostly silver labels; the maker's own benchmark)
