Shifts in political discourse often reflect societal changes, but quantifying how lexical usage and semantic representations vary over time remains a challenge for computational linguistics. To compare approaches under different computational constraints, we propose a cost-aware, modeland language-agnostic framework to compare lexical and semantic change signals. We apply it to Brazilian Portuguese political discourse using a shared panel of lemmas combining drift candidates, stable controls, and theory-driven seeds. We compare three families of diachronic drift signals: a TF-IDF lexical-salience baseline, aligned slice-specific Word2Vec models, and a contextual BERT approach. Results indicate disagreement between cheaper baselines, while contextual BERT remains weakly aligned with each method. A filtered contextual panel reduces stable-control leakage and highlights drift candidates such as “bloqueio” and “salário”. Rather than treating any detector as ground truth, this study presents an exploratory comparison of complementary drift sensitivities, interpretability, and computational cost in Brazilian Portuguese political discourse.