Narratives are a new tool to propagate ideas that are sometimes well hidden in press articles. The SemEval-2025 Task 10 focuses on detecting and extracting such narratives in multiple languages. In this paper, we explore the capabilities of encoder-based language models to classify texts according to the narrative they contain. We show that multilingual encoders outperform monolingual models on this dataset, which is challenging due to the small number of samples per class per language. We perform additional experiments to measure the generalization of features in multilingual models to new languages.