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.
Metask Lab's calibrated decision model in 16 languages. Qwen3.5-4B with a merged LoRA trained with the Nimble candidate-logit objective; one forward pass and a softmax over at most 26 answer-letter logits, with one temperature per question type.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
lumma-fev
metask-jev
Fine-tuned from
frontiersmind/lumma-0.6b-base
qwen/qwen3.5-4b
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
64.0%
80.1%
Calibration error
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Valid action rate
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Median latency
45.8 ms
62.8 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 lumma-fev and metask-jev?
lumma-fev is from FrontiersMind and metask-jev from Metask Lab. Both have open weights you can download and run. Both answer choice, score and noul questions. Only metask-jev answers classify and route. lumma-fev reads up to 8K tokens of state, against 4K tokens for metask-jev. lumma-fev is the smaller model, at 649M parameters to 4.5B.
Which is more accurate, lumma-fev or metask-jev?
They report on different suites — lumma-fev 64.0% on typed-decisions (maker's table; split not stated), metask-jev 80.1% on JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, lumma-fev or metask-jev?
lumma-fev: Free (open weights). metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, lumma-fev or metask-jev?
By their publishers’ figures, lumma-fev answers in about 45.8 ms at the median and metask-jev in about 62.8 ms — measured on different hardware, so treat it as a rough guide.
Can I run lumma-fev or metask-jev locally?
Yes, both: systemone pull frontiersmind/lumma-fev and systemone pull metask-lab/metask-jev download the weights.
Evaluation suite
typed-decisions (maker's table; split not stated)
JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
