Liquid AI's first decision model, served only through the Liquid API. Returns Choice, Score and Noul answers with probability distributions and zero output tokens, on the /v1/systemone request shape used by TypeSafe's SDK.
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.
Decides
choice, score, noul, classify, route
choice, score, noul, classify
Architecture
d1
jevembed
Fine-tuned from
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qwen/qwen3-embedding-4b
License
proprietary
apache-2.0
Availability
Hosted API
Open weights
Hosted by
Liquid AI
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Input price
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Decision accuracy
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85.9%
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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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 d1 and jevembed?
d1 is from Liquid AI and jevembed from HIT-TMG (Lychee Team). d1 is only available as a hosted API; jevembed has open weights you can download and run. Both answer choice, score, noul and classify questions. Only d1 answers route. d1 is licensed proprietary; jevembed, apache-2.0.
Which is more accurate, d1 or jevembed?
Only jevembed publishes an accuracy figure (85.9% on JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging); d1 does not, so there is no comparison to make without your own test.
Which is cheaper, d1 or jevembed?
d1: Hosted, price not published. jevembed: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run d1 or jevembed locally?
jevembed yes — systemone pull hit-tmg/jevembed downloads its weights. The other is only served as a hosted API.
Evaluation suite
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JevEmbed-Data test split (64,110 hard-label questions), final LoRA checkpoint before merging
