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 System One model from Interfaze AI. A rank-32 LoRA on Qwen3.5-4B reads yes/no, choice and score answers from the logits of one forward pass, returns calibrated probabilities and serves the /v1/systemone API.
Decides
choice, score, noul, rank, classify, route
choice, score, noul, classify, route
Architecture
clm
lev
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-4b
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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68.9%
Calibration error
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0.115
Valid action rate
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Median latency
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69 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 lev?
clm is from Contrastive-LM and lev from Interfaze AI. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions. Only clm answers rank. lev is the smaller model, at 4.0B parameters to 8.0B.
Which is more accurate, clm or lev?
Only lev publishes an accuracy figure (68.9% on S1Bench (13 public subsets, macro)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or lev?
clm: Free (open weights). lev: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or lev locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull interfaze-ai/lev download the weights.
