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.
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, noul, route, classify
choice, score, noul
Architecture
decisiontune
lumma-fev
Fine-tuned from
answerdotai/modernbert-large
frontiersmind/lumma-0.6b-base
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
—
64.0%
Calibration error
—
—
Valid action rate
—
—
Median latency
—
45.8 ms
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 lumma-fev?
decisiontune is from DecisionTune and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice and noul questions. Only decisiontune answers route and classify. Only lumma-fev answers score. decisiontune is the smaller model, at 395M parameters to 649M.
Which is more accurate, decisiontune or lumma-fev?
Only lumma-fev publishes an accuracy figure (64.0% on typed-decisions (maker's table; split not stated)); decisiontune does not, so there is no comparison to make without your own test.
Which is cheaper, decisiontune or lumma-fev?
decisiontune: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run decisiontune or lumma-fev locally?
Yes, both: systemone pull decision-tune/decisiontune and systemone pull frontiersmind/lumma-fev download the weights.
