A family of open System One models by Mark Marosi, decider-0.8b, decider-2b, decider-4b and decider-35b-a3b on Qwen3.5 bases, that softmax letter logits at an answer slot. This page carries decider-2b v11.
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.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, extract, route
Architecture
decider
gliner2
Fine-tuned from
qwen/qwen3.5-2b-base
fastino/gliner2-large-v1
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
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Fastino
Input price
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Decision accuracy
75.2%
60.2%
Calibration error
—
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Valid action rate
—
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Median latency
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38.3 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 decider and gliner2-5-decide?
decider is from Mapika and gliner2-5-decide from Fastino Labs. decider has open weights you can download and run; gliner2-5-decide has open weights and a hosted API. Both answer choice, score, noul, classify and route questions. Only gliner2-5-decide answers extract. gliner2-5-decide is the smaller model, at 340M parameters to 1.9B.
Which is more accurate, decider or gliner2-5-decide?
They report on different suites — decider 75.2% on Decider regression set, 28 held-out tasks (decider-2b v11), gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, decider or gliner2-5-decide?
decider: Free (open weights). gliner2-5-decide: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run decider or gliner2-5-decide locally?
Yes, both: systemone pull mapika/decider and systemone pull fastino-labs/gliner2-5-decide download the weights.
