xlm-roberta-base and distilbert-base-uncased are both fill-mask models. See each entry for specifics.
XLM-RoBERTa base from Facebook AI, pre-trained on 2.5TB of filtered CommonCrawl text across 100 languages using the RoBERTa training procedure. Enables cross-lingual transfer — models fine-tuned on labeled English data can infer on other languages without parallel annotations. The standard starting point for multilingual classification and token-level tasks.
DistilBERT-base-uncased is a distilled version of BERT-base-uncased, 40% smaller and 60% faster while retaining approximately 97% of BERT's language understanding performance on the GLUE benchmark. Trained via knowledge distillation from BERT using BookCorpus and Wikipedia. Commonly used when BERT's performance is needed but inference speed or resource constraints are limiting factors.
Pick based on your compute budget and specific task requirements.