AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
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.
Decides
choice, score, noul
choice, score, noul, classify
Architecture
blocks-of-experts
open-jev
Fine-tuned from
qwen/qwen3.8-27b
microsoft/deberta-v3-large
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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—
Input price
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Decision accuracy
88.7%
85.4%
Calibration error
—
0.022
Valid action rate
—
—
Median latency
137 ms
28 ms
p95 latency
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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 jev-27b and open-jev-deberta-v3-large?
jev-27b is from AutoTrust AI Lab and open-jev-deberta-v3-large from Kotoba Labs. 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 27B.
Which is more accurate, jev-27b or open-jev-deberta-v3-large?
They report on different suites — jev-27b 88.7% on JevBench public set (231 items), family-macro score, maker's run, open-jev-deberta-v3-large 85.4% on Kotoba held-out test, seen question types (banking77, SST-5, BoolQ) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jev-27b or open-jev-deberta-v3-large?
jev-27b: Free (open weights). open-jev-deberta-v3-large: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, jev-27b or open-jev-deberta-v3-large?
By their publishers’ figures, open-jev-deberta-v3-large answers in about 28 ms at the median and jev-27b in about 137 ms — measured on different hardware, so treat it as a rough guide.
Can I run jev-27b or open-jev-deberta-v3-large locally?
Yes, both: systemone pull autotrust-ai/jev-27b and systemone pull kotoba-labs/open-jev-deberta-v3-large download the weights.
Evaluation suite
JevBench public set (231 items), family-macro score, maker's run
Kotoba held-out test, seen question types (banking77, SST-5, BoolQ)
