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.
A 4B decision model fine-tuned from Qwen3.5-4B-Base. A state and typed questions (choice, yes/no, score) go in; a temperature-calibrated probability for every option comes out of one pass, through a self-hosted /v1/systemone-compatible server. Apache-2.0 weights and code.
Decides
choice, score, noul, classify, extract, route
choice, score, noul, classify, route
Architecture
gliner2
wald
Fine-tuned from
fastino/gliner2-large-v1
qwen/qwen3.5-4b-base
License
apache-2.0
apache-2.0
Availability
Open weights + hosted API
Open weights
Hosted by
Fastino
—
Input price
—
—
Decision accuracy
60.2%
88.3%
Calibration error
—
0.045
Valid action rate
—
—
Median latency
38.3 ms
33 ms
p95 latency
—
168 ms
Evaluation suite
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 wald?
gliner2-5-decide is from Fastino Labs and wald from ORG2 AI. gliner2-5-decide has open weights and a hosted API; wald 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 4.2B.
Which is more accurate, gliner2-5-decide or wald?
They report on different suites — gliner2-5-decide 60.2% on Fastino fast-decisions suite (17 datasets), wald 88.3% on JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, gliner2-5-decide or wald?
gliner2-5-decide: Hosted, price not published, or free to self-host. wald: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, gliner2-5-decide or wald?
By their publishers’ figures, wald answers in about 33 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 wald locally?
Yes, both: systemone pull fastino-labs/gliner2-5-decide and systemone pull org2ai/wald download the weights.
Fastino fast-decisions suite (17 datasets)
JevBench public set (231 items; 204 correct), maker's run with the JevBench CLI, v1.2, effort none; items used as a development scoreboard
Latest version
2026.09.25
1.2.0
Variants
encoder_config
Dockerfile, LICENSE, NOTICE, model-files, reference, server
