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.
Uprelic's hosted decision API, run on GPUs in Paris. Answers noul, choice and score questions about text, JSON and up to 8 images, with a probability for every option and no generated text, on the same request shape as Jev. No weights; base model and size not disclosed.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
strom
Fine-tuned from
fastino/gliner2-large-v1
—
License
apache-2.0
proprietary
Availability
Open weights + hosted API
Hosted API
Hosted by
Fastino
Uprelic
Input price
—
$0.042/MTok
Decision accuracy
60.2%
87.0%
Calibration error
—
0.033
Valid action rate
—
—
Median latency
38.3 ms
136 ms
p95 latency
—
—
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 strom?
gliner2-5-decide is from Fastino Labs and strom from Uprelic. gliner2-5-decide has open weights and a hosted API; strom is only available as a hosted API. Both answer choice, score, noul, classify and route questions. Only gliner2-5-decide answers extract. gliner2-5-decide is licensed apache-2.0; strom, proprietary.
Which is more accurate, gliner2-5-decide or strom?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), strom 87.0% on JevBench public set (231 items), the maker's production run of Strom 1.0.7 on 1 Oct 2026 (Open-Jev harness format) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or strom?
gliner2-5-decide: Hosted, price not published, or free to self-host. strom: $0.042 / $0 per 1M. Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or strom?
By their publishers’ figures, gliner2-5-decide answers in about 38.3 ms at the median and strom in about 136 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or strom locally?
gliner2-5-decide yes — systemone pull fastino-labs/gliner2-5-decide downloads its weights. The other is only served as a hosted API.
JevBench public set (231 items), the maker's production run of Strom 1.0.7 on 1 Oct 2026 (Open-Jev harness format)
