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.
An open-weights System One model from Convai Innovations. A fully fine-tuned ModernBERT-large encoder with a from-scratch decision head that scores one marker per option and answers every question in a single 33–39 ms pass. Runs on your own CPU or GPU.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify, route
Architecture
clm
laya
Fine-tuned from
qwen/qwen3-8b
answerdotai/modernbert-large
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
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Calibration error
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Valid action rate
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Median latency
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39.5 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 clm and laya?
clm is from Contrastive-LM and laya from Convai Innovations. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. Only clm answers rank. laya is the smaller model, at 421M parameters to 8.0B.
Which is more accurate, clm or laya?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, clm or laya?
clm: Free (open weights). laya: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or laya locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull convai-innovations/laya download the weights.