This study evaluates biomedical language models for sentence-level adverse drug event detection in regulated pharmacovigilance settings. Using the deduplicated ADE-Corpus-V2 dataset, models from 110M to 7B parameters were compared under a fixed single-GPU setup with five random seeds. The evaluation considered regulatory needs such as reproducibility, traceability, validation, and change control. The results show that mid-sized biomedical pretrained language models, especially 300–400M parameter models, achieved strong and stable performance. Larger models did not clearly outperform them. These findings suggest that reliable ADE classification can be achieved with practical, auditable models without relying on very large proprietary LLMs.