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.
Together AI's Jev-style classifier. A LoRA fine-tune of Qwen3.5-4B that reads a state, a question and 2 to 24 options and returns one option letter. Served on Together's platform; recipe published.
Decides
choice, score, noul, rank, classify, route
choice, classify, route
Architecture
clm
tev
Fine-tuned from
qwen/qwen3-8b
qwen/qwen3.5-4b
License
apache-2.0
Unspecified — weights licence being finalised
Availability
Open weights
Open weights + hosted API
Hosted by
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Together AI
Input price
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$0.042/MTok
Decision accuracy
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88.0%
Calibration error
—
—
Valid action rate
—
—
Median latency
—
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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 tev1?
clm is from Contrastive-LM and tev1 from Together AI. clm has open weights you can download and run; tev1 has open weights and a hosted API. Both answer choice, classify and route questions. Only clm answers score, noul and rank. tev1 is the smaller model, at 4.0B parameters to 8.0B. clm is licensed apache-2.0; tev1, other.
Which is more accurate, clm or tev1?
Only tev1 publishes an accuracy figure (88.0% on Together development set (reused, not held out)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or tev1?
clm: Free (open weights). tev1: $0.042 / $0 per 1M, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run clm or tev1 locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull together-ai/tev1 download the weights.
