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.
A ModernBERT-large encoder with an option-marker head. Premise and options are packed into one sequence and each option's marker is scored in a single bidirectional pass; version 1.2 makes the scoring order-invariant.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify
Architecture
clm
von
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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63.9%
Calibration error
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0.045
Valid action rate
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Median latency
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18 ms
p95 latency
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Evaluation suite
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 von?
clm is from Contrastive-LM and von from wfzyx. Both have open weights you can download and run. Both answer choice, score, noul and classify questions. Only clm answers rank and route. von is the smaller model, at 395M parameters to 8.0B.
Which is more accurate, clm or von?
Only von publishes an accuracy figure (63.9% on JevBench public standard tier); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or von?
clm: Free (open weights). von: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or von locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull wfzyx/von download the weights.
