An embedding model turned decision model. Qwen3-Embedding-4B with a merged LoRA, driven by the open JevEmbed toolkit, which scores Choice, Score and Noul questions from embedding similarity and returns a distribution without generating text.
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, classify
choice, score, noul, classify, route
Architecture
jevembed
metask-jev
Fine-tuned from
qwen/qwen3-embedding-4b
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
85.9%
80.1%
Calibration error
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Valid action rate
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Median latency
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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 jevembed and metask-jev?
jevembed is from HIT-TMG (Lychee Team) and metask-jev from Metask Lab. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only metask-jev answers route. metask-jev reads up to 4K tokens of state, against 1K tokens for jevembed. jevembed is the smaller model, at 4.0B parameters to 4.5B.
Which is more accurate, jevembed or metask-jev?
They report on different suites — jevembed 85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging, 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, jevembed or metask-jev?
jevembed: Free (open weights). metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run jevembed or metask-jev locally?
Yes, both: systemone pull hit-tmg/jevembed and systemone pull metask-lab/metask-jev download the weights.
Evaluation suite
JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
