Caliber Flex 1B by OnePass. Decision-only model, exactly 1,000,000,000 parameters.
Ruoxi Qiu's decision adapter for a frozen Qwen3.8-27B: a 6.3M-parameter LoRA on the last four text layers plus a numerical prior. One shared state feeds isolated question branches; each Choice, Noul or Score question returns probabilities with no generated tokens.
Decides
choice, noul, score
choice, noul, score
Architecture
llm
canopy-jev
Fine-tuned from
qwen/qwen3.5-0.8b-base
qwen/qwen3.8-27b
License
caliber-flex-non-commercial-1.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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87.4%
Calibration error
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Valid action rate
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Median latency
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p95 latency
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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 caliber-flex-1b and canopy-jev?
caliber-flex-1b is from onepass and canopy-jev from Ruoxi Qiu. Both have open weights you can download and run. Both answer choice, noul and score questions. caliber-flex-1b is licensed other; canopy-jev, apache-2.0.
Which is more accurate, caliber-flex-1b or canopy-jev?
Only canopy-jev publishes an accuracy figure (87.4% on JevBench public set (231 items; 202 correct), the maker's own fresh zero-shot run with the upstream scorer); caliber-flex-1b does not, so there is no comparison to make without your own test.
Which is cheaper, caliber-flex-1b or canopy-jev?
caliber-flex-1b: Free (open weights). canopy-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run caliber-flex-1b or canopy-jev locally?
Yes, both: systemone pull onepass/caliber-flex-1b and systemone pull camellia86/canopy-jev download the weights.
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JevBench public set (231 items; 202 correct), the maker's own fresh zero-shot run with the upstream scorer
