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.
Standard Thinking's decision model. Ministral 3 8B with a merged LoRA, served through stock SGLang and an open jev-adapter that exposes /v1/systemone and scores the supplied options in one forward pass with per-type temperatures.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
standard-one
Fine-tuned from
fastino/gliner2-large-v1
mistralai/ministral-3-8b-instruct-2512-bf16
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights + hosted API
Hosted by
Fastino
Standard Thinking
Input price
—
—
Decision accuracy
60.2%
71.1%
Calibration error
—
—
Valid action rate
—
—
Median latency
38.3 ms
25.8 ms
p95 latency
—
41.9 ms
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 standard-one?
gliner2-5-decide is from Fastino Labs and standard-one from Standard Thinking. Both have 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 8.0B.
Which is more accurate, gliner2-5-decide or standard-one?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), standard-one 71.1% on typed-decisions suite, 400 cases / 2,000 decisions (maker's served run, v2) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or standard-one?
gliner2-5-decide: Hosted, price not published, or free to self-host. standard-one: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or standard-one?
By their publishers’ figures, standard-one answers in about 25.8 ms at the median and gliner2-5-decide in about 38.3 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or standard-one locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull standard-thinking/standard-one download the weights.
