Fastino's open-weight decision model. A DeBERTa-v3-large encoder that scores label sets supplied at runtime, answers single-label, multi-label, yes/no and ordinal questions in one pass, and extracts spans and relations.
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, extract, route
choice, score, noul
Architecture
gliner2
lumma-fev
Fine-tuned from
fastino/gliner2-large-v1
frontiersmind/lumma-0.6b-base
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Fastino
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Input price
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Decision accuracy
60.2%
64.0%
Calibration error
—
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Valid action rate
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Median latency
38.3 ms
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 gliner2-5-decide and lumma-fev?
gliner2-5-decide is from Fastino Labs and lumma-fev from FrontiersMind. gliner2-5-decide has open weights and a hosted API; lumma-fev has open weights you can download and run. Both answer choice, score and noul questions. Only gliner2-5-decide answers classify, extract and route. gliner2-5-decide is the smaller model, at 340M parameters to 649M.
Which is more accurate, gliner2-5-decide or lumma-fev?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), lumma-fev 64.0% on typed-decisions (maker's table; split not stated) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or lumma-fev?
gliner2-5-decide: Hosted, price not published, or free to self-host. lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or lumma-fev?
By their publishers’ figures, gliner2-5-decide answers in about 38.3 ms at the median and lumma-fev in about 45.8 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or lumma-fev locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull frontiersmind/lumma-fev download the weights.
