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.
Featherless AI's open server that turns a stock Hugging Face language model into a typed-decision endpoint. It reads the next-token logits for each question and builds the Choice, Score or Noul answer itself; no classifier head is trained and no JSON is generated.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
simple-jev
Fine-tuned from
fastino/gliner2-large-v1
google/gemma-4-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights + hosted API
Hosted by
Fastino
Featherless AI
Input price
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Decision accuracy
60.2%
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Calibration error
—
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Valid action rate
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Median latency
38.3 ms
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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 simple-jev?
gliner2-5-decide is from Fastino Labs and simple-jev from Featherless AI. Both have open weights and a hosted API. Both answer choice, score, noul, classify and route questions. Only gliner2-5-decide answers extract.
Which is more accurate, gliner2-5-decide or simple-jev?
Only gliner2-5-decide publishes an accuracy figure (60.2% on Fastino fast-decisions suite (17 datasets)); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, gliner2-5-decide or simple-jev?
gliner2-5-decide: Hosted, price not published, or free to self-host. simple-jev: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run gliner2-5-decide or simple-jev locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull featherless-ai/simple-jev download the weights.
