This work investigates how pretrained language models (PLMs) encode character-level information despite using subword tokenization. By probing embeddings from models like GPT-J, BERT, and RoBERTa, the study finds that PLMs reliably capture whether specific characters appear in a token—even across non-Latin scripts. Larger models generally encode this information more robustly. The analysis suggests this ability arises from patterns in tokenization, character–POS correlations, and language variability.