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.
Shanghai AI Laboratory's multimodal decision model. A fine-tune of Qwen3.5-4B's language backbone (vision tower frozen) that answers up to 16 Choice, Score and Noul questions about a state and up to eight images in one forward pass, with a fitted calibration temperature.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
intern-decision
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%
80.6%
Calibration error
—
—
Valid action rate
—
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Median latency
38.3 ms
44 ms
p95 latency
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44.6 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 intern-decision?
gliner2-5-decide is from Fastino Labs and intern-decision from InternLM (Shanghai AI Laboratory). gliner2-5-decide has open weights and a hosted API; intern-decision 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 4.5B.
Which is more accurate, gliner2-5-decide or intern-decision?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), intern-decision 80.6% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or intern-decision?
gliner2-5-decide: Hosted, price not published, or free to self-host. intern-decision: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or intern-decision?
By their publishers’ figures, gliner2-5-decide answers in about 38.3 ms at the median and intern-decision in about 44 ms — measured on different hardware, so treat it as a rough guide.
Can I run gliner2-5-decide or intern-decision locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull internlm/intern-decision download the weights.
Evaluation suite
Fastino fast-decisions suite (17 datasets)
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions)
