Frontier Infra's open decision model. A LoRA merged into Qwen3.8-27B (thinking off) that answers one typed question per forward pass with a probability over the option labels, served through AINode's TypeSafe-compatible /v1/systemone.
FrontiersMind's decision model on a base it pre-trained from scratch. Each question reads the state and its own tokens once and a 256-dimension pointer head scores the options; Choice and Score take up to 255 entries, and nothing is generated.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
jebadiah
lumma-fev
Fine-tuned from
qwen/qwen3.8-27b
frontiersmind/lumma-0.6b-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
86.6%
64.0%
Calibration error
0.113
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Valid action rate
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Median latency
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45.8 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 jebadiah and lumma-fev?
jebadiah is from Frontier Infra and lumma-fev from FrontiersMind. Both have open weights you can download and run. Both answer choice, score and noul questions. Only jebadiah answers classify and route. lumma-fev is the smaller model, at 649M parameters to 27B.
Which is more accurate, jebadiah or lumma-fev?
They report on different suites — jebadiah 86.6% on JevBench public set (231 items), maker's run with the v1.4.2 harness; ECE on the hard tier, lumma-fev 64.0% on typed-decisions (maker's table; split not stated) — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, jebadiah or lumma-fev?
jebadiah: Free (open weights). lumma-fev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jebadiah or lumma-fev locally?
Yes, both: systemone pull frontier-infra/jebadiah and systemone pull frontiersmind/lumma-fev download the weights.
Evaluation suite
JevBench public set (231 items), maker's run with the v1.4.2 harness; ECE on the hard tier
