Providers

Drift rate

detected changes per endpoint-year of LT or B3IT monitoring — normalized for fleet size & monitoring length — with a 95% Poisson interval. Providers are grouped by company; their serving variants (fp8, fp4, …) are compared on the provider page
LT B3IT Both

All providers

including the ones without enough monitoring for a rate yet, with both methods' rates side by side; 1 square = one endpoint-month of LT monitoring
Provider Endpoints LT drift rate LT monitoring B3IT drift rate B3IT monitoring Last change

Timothée Chauvin, Erwan Le Merrer, François Taïani, Gilles Tredan (2026), "Log Probability Tracking of LLM APIs". ICLR 2026. https://arxiv.org/abs/2512.03816

Timothée Chauvin, Clément Lalanne, Erwan Le Merrer, Jean-Michel Loubes, François Taïani, Gilles Tredan (2026), "Token-Efficient Change Detection in LLM APIs". ICML 2026. https://arxiv.org/abs/2602.11083

@inproceedings{
chauvin2026log,
title={Log Probability Tracking of {LLM} {API}s},
author={Timothee Chauvin and Erwan Le Merrer and Francois Taiani and Gilles Tredan},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=hFxivbAgVP}
}

@inproceedings{
chauvin2026tokenefficient,
title={Token-Efficient Change Detection in {LLM} {API}s},
author={Timothee Chauvin and Cl{\'e}ment Lalanne and Erwan Le Merrer and Jean-Michel Loubes and Francois Taiani and Gilles Tredan},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=7cMlZZYZT0}
}