The analysis of multilingual corpora with respect to political stance is of great interest to political science. However, it is a pri- ori unclear whether any differences found are genuinely cross-lingual in nature or artifacts of imperfect alignment, in particular for low- resource languages. In this study, we consider political actor portrayal in Dari and Pashto, the two main languages of Afghanistan. We work with a corpus of 200 quasi-parallel Dari–Pashto article pairs from a single outlet and evaluate the ability of three multilingual sentence-embedding models to make consistent cross-lingual predictions. We find that actor-level consistency differs substantially across models, and identical predicted labels can conceal differences in the underlying label-score profiles. We conclude that agreement alone is therefore insufficient for interpreting cross-lingual actor-portrayal predictions. This underlines the need for human validation before automatic cross-lingual predictions are used to support substantive conclusions about actor portrayal.