UOL@IDEM presents a closed-track submission to BEA 2026, modeling vocabulary difficulty prediction as regression for Spanish, German, and Chinese. The system integrates multilingual contextual embeddings with engineered features like frequency and cognate similarity, achieving lower RMSE scores than baselines, with feature analysis highlighting frequency as the most stable predictor and contextual predictability as a key L1-sensitive signal.