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.
FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
Decides
choice, score, noul, classify, rank, route
choice, score, noul
Architecture
arc-decide
lumma-fev
Fine-tuned from
qwen/qwen3-0.6b-base
frontiersmind/lumma-0.6b-base
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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64.0%
Calibration error
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Valid action rate
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Median latency
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45.8 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 lumma-fev?
arc-decide is from Arc53 and lumma-fev from FrontiersMind. 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 649M. arc-decide is licensed mit; lumma-fev, apache-2.0.
Which is more accurate, arc-decide or lumma-fev?
Only lumma-fev publishes an accuracy figure (64.0% on typed-decisions (maker's table; split not stated)); arc-decide does not, so there is no comparison to make without your own test.
Which is cheaper, arc-decide or lumma-fev?
arc-decide: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run arc-decide or lumma-fev locally?
Yes, both: systemone pull arc53/arc-decide and systemone pull frontiersmind/lumma-fev download the weights.
