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.
An open System One model from Interfaze AI. A rank-32 LoRA on Qwen3.5-4B reads yes/no, choice and score answers from the logits of one forward pass, returns calibrated probabilities and serves the /v1/systemone API.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
lev
Fine-tuned from
fastino/gliner2-large-v1
qwen/qwen3.5-4b
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%
68.9%
Calibration error
—
0.115
Valid action rate
—
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Median latency
38.3 ms
69 ms
p95 latency
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Evaluation suite
Fastino fast-decisions suite (17 datasets)
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 lev?
gliner2-5-decide is from Fastino Labs and lev from Interfaze AI. gliner2-5-decide has open weights and a hosted API; lev has open weights you can download and run. 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 4.0B.
Which is more accurate, gliner2-5-decide or lev?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), lev 68.9% on S1Bench (13 public subsets, macro) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or lev?
gliner2-5-decide: Hosted, price not published, or free to self-host. lev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or lev?
By their publishers’ figures, gliner2-5-decide answers in about 38.3 ms at the median and lev in about 69 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or lev locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull interfaze-ai/lev download the weights.
