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.
DecisionTune's 395M decision model: ModernBERT-large fine-tuned with a 4 KB scoring head that scores a marker per option, answering a choice or yes/no question in one encoder pass with a probability for every option. Runs locally on CPU or GPU via PyTorch, MLX or ONNX.
Decides
choice, score, noul, rank, classify, route
choice, noul, route, classify
Architecture
clm
decisiontune
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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p95 latency
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 decisiontune?
clm is from Contrastive-LM and decisiontune from DecisionTune. Both have open weights you can download and run. Both answer choice, noul, classify and route questions. Only clm answers score and rank. decisiontune is the smaller model, at 395M parameters to 8.0B.
Which is more accurate, clm or decisiontune?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, clm or decisiontune?
clm: Free (open weights). decisiontune: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or decisiontune locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull decision-tune/decisiontune download the weights.
