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.
AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
Decides
choice, score, noul, rank, classify, route
choice, score, noul
Architecture
clm
blocks-of-experts
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.8-27b
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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88.7%
Calibration error
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Valid action rate
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Median latency
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137 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 jev-27b?
clm is from Contrastive-LM and jev-27b from AutoTrust AI Lab. Both have open weights you can download and run. Both answer choice, score and noul questions. Only clm answers rank, classify and route. clm is the smaller model, at 8.0B parameters to 27B.
Which is more accurate, clm or jev-27b?
Only jev-27b publishes an accuracy figure (88.7% on JevBench public set (231 items), family-macro score, maker's run); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or jev-27b?
clm: Free (open weights). jev-27b: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or jev-27b locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull autotrust-ai/jev-27b download the weights.
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Evaluation suite
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JevBench public set (231 items), family-macro score, maker's run
