Contrastive Language Models score a state against a set of candidate actions with a contrastive objective. Two projection heads on frozen Qwen3-8B embeddings, trained with InfoNCE; clm-serve maps Choice, Score and Noul onto candidate ranking.
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.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify, route
Architecture
clm
d1
Fine-tuned from
qwen/qwen3-8b
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License
apache-2.0
proprietary
Availability
Open weights
Hosted API
Hosted by
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Liquid AI
Input price
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Decision accuracy
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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 clm and d1?
clm is from Contrastive-LM and d1 from Liquid AI. clm has open weights you can download and run; d1 is only available as a hosted API. Both answer choice, score, noul, classify and route questions. Only clm answers rank. clm is licensed apache-2.0; d1, proprietary.
Which is more accurate, clm or d1?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, clm or d1?
clm: Free (open weights). d1: Hosted, price not published. Open weights cost nothing per call beyond your own hardware.
Can I run clm or d1 locally?
clm yes — systemone pull contrastive-lm/clm downloads its weights. The other is only served as a hosted API.
