Romanian orthography relies on five diacritic characters (ă, î, â, ş, ţ) that are frequently omitted in informal digital text, yet the impact of text cleaning decisions regarding diacritics on Romanian language models has not been systematically studied across architectures and tasks. We address this gap by examining how diacritics affect subword tokenization, contextual embeddings, and three downstream tasks - POS tagging, named entity recognition, and semantic textual similarity - comparing Romanian BERT with mBERT, DistilmBERT, and mT5. Each model is fine-tuned on diacriticpreserving and diacritic-stripped data and evaluated on both, isolating both overall performance and robustness to train–test mismatch. This work provides the first systematic, multitask account of how diacritic effects propagate from tokenization to downstream performance, showing this depends jointly on pretraining corpus and architecture, and offers practical guidance for Romanian NLP systems that must handle diacritic-inconsistent text.