An independent Jev-shaped reproduction on DeBERTa-v3-large. One state and any number of choice, score and yes/no questions go in, and a calibrated distribution per question comes out of one forward pass. Public gold labels only.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, classify
choice, score, noul
Architecture
open-jev
this-that
Fine-tuned from
microsoft/deberta-v3-large
mapika/decider
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
85.4%
87.8%
Calibration error
0.022
—
Valid action rate
—
—
Median latency
28 ms
31.4 ms
p95 latency
—
—
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 open-jev-deberta-v3-large and this-that-model?
open-jev-deberta-v3-large is from Kotoba Labs and this-that-model from FLock.io. Both have open weights you can download and run. Both answer choice, score and noul questions. Only open-jev-deberta-v3-large answers classify. open-jev-deberta-v3-large is the smaller model, at 434M parameters to 1.9B. open-jev-deberta-v3-large is licensed apache-2.0; this-that-model, mit.
Which is more accurate, open-jev-deberta-v3-large or this-that-model?
They report on different suites — open-jev-deberta-v3-large 85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ), this-that-model 87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, open-jev-deberta-v3-large or this-that-model?
open-jev-deberta-v3-large: Free (open weights). this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, open-jev-deberta-v3-large or this-that-model?
By their publishers’ figures, open-jev-deberta-v3-large answers in about 28 ms at the median and this-that-model in about 31.4 ms — measured on different hardware, so treat it as a rough guide.
Can I run open-jev-deberta-v3-large or this-that-model locally?
Yes, both: systemone pull kotoba-labs/open-jev-deberta-v3-large and systemone pull flock-io/this-that-model download the weights.
Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
