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 open family of System One models. A LoRA adapter plus a pointer head on a frozen Qwen base returns a distribution per typed question in one forward pass, serves TypeSafe's /v1/systemone contract, and ships a fitted temperature with every checkpoint.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
blocks-of-experts
kev
Fine-tuned from
qwen/qwen3.8-27b
qwen/qwen3.5-4b-base
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%
83.8%
Calibration error
—
0.042
Valid action rate
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Median latency
137 ms
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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 kev?
jev-27b is from AutoTrust AI Lab and kev from Jared Palmer. Both have open weights you can download and run. Both answer choice, score and noul questions. Only kev answers classify and route. kev is the smaller model, at 4.0B parameters to 27B.
Which is more accurate, jev-27b or kev?
They report on different suites — jev-27b 88.7% on JevBench public set (231 items), family-macro score, maker's run, kev 83.8% on transfer-v4 (locked, out of domain) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jev-27b or kev?
jev-27b: Free (open weights). kev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jev-27b or kev locally?
Yes, both: systemone pull autotrust-ai/jev-27b and systemone pull jared-palmer/kev download the weights.
Evaluation suite
JevBench public set (231 items), family-macro score, maker's run
