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.
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, route
choice, score, noul, classify, route
Architecture
d1
metask-jev
Fine-tuned from
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qwen/qwen3.5-4b
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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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 d1 and metask-jev?
d1 is from Liquid AI and metask-jev from Metask Lab. d1 is only available as a hosted API; metask-jev has open weights you can download and run. Both answer choice, score, noul, classify and route questions. d1 is licensed proprietary; metask-jev, apache-2.0.
Which is more accurate, d1 or metask-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); d1 does not, so there is no comparison to make without your own test.
Which is cheaper, d1 or metask-jev?
d1: Hosted, price not published. metask-jev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run d1 or metask-jev locally?
metask-jev yes — systemone pull metask-lab/metask-jev downloads its weights. The other is only served as a hosted API.
Evaluation suite
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JevBench v1.2 public set (231 items) at 4,096 tokens, maker's run
