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.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, classify, extract, route
choice, score, noul
Architecture
gliner2
this-that
Fine-tuned from
fastino/gliner2-large-v1
mapika/decider
License
apache-2.0
mit
Availability
Open weights + hosted API
Open weights
Hosted by
Fastino
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Input price
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Decision accuracy
60.2%
87.8%
Calibration error
—
—
Valid action rate
—
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Median latency
38.3 ms
31.4 ms
p95 latency
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Evaluation suite
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 this-that-model?
gliner2-5-decide is from Fastino Labs and this-that-model from FLock.io. gliner2-5-decide has open weights and a hosted API; this-that-model 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 1.9B. gliner2-5-decide is licensed apache-2.0; this-that-model, mit.
Which is more accurate, gliner2-5-decide or this-that-model?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), this-that-model 87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or this-that-model?
gliner2-5-decide: Hosted, price not published, or free to self-host. this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or this-that-model?
By their publishers’ figures, this-that-model answers in about 31.4 ms at the median and gliner2-5-decide in about 38.3 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or this-that-model locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull flock-io/this-that-model download the weights.
