Berget AI's System One model for gating agent commands: a LoRA adapter and a fine-tuned joint schema head on Cloudflare's Clef-Flash that answer noul, choice and score questions over a state in one forward pass. Trained on Swedish and English operations decisions.
SimpleJev's LoRA adapters and pointer head that turn an open model into a typed-decision model: given a state, a question and the options, it returns a probability for every option without generating text. This entry is the Qwen3.8-27B release; the others are linked.
Decides
noul, choice, score
choice, score, noul
Architecture
clef
jevany
Fine-tuned from
cloudflare/clef-flash
qwen/qwen3.8-27b
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
97.0%
86.0%
Calibration error
—
0.026
Valid action rate
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Median latency
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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 bev and jevany?
bev is from Berget AI and jevany from SimpleJev. Both have open weights you can download and run. Both answer noul, choice and score questions. bev is the smaller model, at 9.0B parameters to 27B.
Which is more accurate, bev or jevany?
They report on different suites — bev 97.0% on Berget held-out risk split (16,902 questions; same operations-traffic corpora as training), jevany 86.0% on SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, bev or jevany?
bev: Free (open weights). jevany: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run bev or jevany locally?
Yes, both: systemone pull berget-ai/bev and systemone pull simplejev/jevany download the weights.
Evaluation suite
Berget held-out risk split (16,902 questions; same operations-traffic corpora as training)
SimpleJev Transfer suite (1,046 decisions from seven cross-domain datasets plus robustness slices; the maker's own)