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.
FLock.io's typed-decision model, fine-tuned from Mapika's decider-2b. Reads one hidden state per question and scores the declared options, so an answer outside the list cannot occur; several questions about one state share a forward pass.
Decides
choice, score, noul, rank, classify, route
choice, score, noul
Architecture
clm
this-that
Fine-tuned from
qwen/qwen3-8b
mapika/decider
License
apache-2.0
mit
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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87.8%
Calibration error
—
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Valid action rate
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Median latency
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31.4 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 this-that-model?
clm is from Contrastive-LM and this-that-model from FLock.io. Both have open weights you can download and run. Both answer choice, score and noul questions. Only clm answers rank, classify and route. this-that-model is the smaller model, at 1.9B parameters to 8.0B. clm is licensed apache-2.0; this-that-model, mit.
Which is more accurate, clm or this-that-model?
Only this-that-model publishes an accuracy figure (87.8% on limberc/this-that-complex-decisions (1,710 questions, FLock's benchmark)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or this-that-model?
clm: Free (open weights). this-that-model: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or this-that-model locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull flock-io/this-that-model download the weights.
