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).
Featherless AI's open server that turns a stock Hugging Face language model into a typed-decision endpoint. It reads the next-token logits for each question and builds the Choice, Score or Noul answer itself; no classifier head is trained and no JSON is generated.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
blocks-of-experts
simple-jev
Fine-tuned from
qwen/qwen3.8-27b
google/gemma-4-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
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Featherless AI
Input price
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Decision accuracy
88.7%
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Calibration error
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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 simple-jev?
jev-27b is from AutoTrust AI Lab and simple-jev from Featherless AI. jev-27b has open weights you can download and run; simple-jev has open weights and a hosted API. Both answer choice, score and noul questions. Only simple-jev answers classify and route.
Which is more accurate, jev-27b or simple-jev?
Only jev-27b publishes an accuracy figure (88.7% on JevBench public set (231 items), family-macro score, maker's run); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, jev-27b or simple-jev?
jev-27b: Free (open weights). simple-jev: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run jev-27b or simple-jev locally?
Yes, both: systemone pull autotrust-ai/jev-27b and systemone pull featherless-ai/simple-jev download the weights.
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Evaluation suite
JevBench public set (231 items), family-macro score, maker's run
