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.
AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
Decides
choice, score, noul, classify, extract, route
choice, score, noul
Architecture
gliner2
blocks-of-experts
Fine-tuned from
fastino/gliner2-large-v1
qwen/qwen3.8-27b
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%
88.7%
Calibration error
—
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Valid action rate
—
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Median latency
38.3 ms
137 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 jev-27b?
gliner2-5-decide is from Fastino Labs and jev-27b from AutoTrust AI Lab. gliner2-5-decide has open weights and a hosted API; jev-27b 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 27B.
Which is more accurate, gliner2-5-decide or jev-27b?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), jev-27b 88.7% on JevBench public set (231 items), family-macro score, maker's run — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or jev-27b?
gliner2-5-decide: Hosted, price not published, or free to self-host. jev-27b: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or jev-27b?
By their publishers’ figures, gliner2-5-decide answers in about 38.3 ms at the median and jev-27b in about 137 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or jev-27b locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull autotrust-ai/jev-27b download the weights.
Evaluation suite
Fastino fast-decisions suite (17 datasets)
JevBench public set (231 items), family-macro score, maker's run
