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.
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, classify, route
choice, score, noul, classify, route
Architecture
metask-jev
simple-jev
Fine-tuned from
qwen/qwen3.5-4b
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
80.1%
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Calibration error
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Valid action rate
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Median latency
62.8 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 metask-jev and simple-jev?
metask-jev is from Metask Lab and simple-jev from Featherless AI. metask-jev has open weights you can download and run; simple-jev has open weights and a hosted API. Both answer choice, score, noul, classify and route questions.
Which is more accurate, metask-jev or simple-jev?
Only metask-jev publishes an accuracy figure (80.1% on JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run); simple-jev does not, so there is no comparison to make without your own test.
Which is cheaper, metask-jev or simple-jev?
metask-jev: 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 metask-jev or simple-jev locally?
Yes, both: systemone pull metask-lab/metask-jev and systemone pull featherless-ai/simple-jev download the weights.
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Evaluation suite
JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run