Machine translation, and more specifically neural machine translation (NMT), have been proven to be subject to gender bias in recent years. Following previous studies’ methodology, we rely on a test suite formed with occupational nouns to investigate, through human evaluation, the influence of two different potential factors on gender bias occurrence in generic NMT: stereotypes and language combinations. Similarly to previous findings, we confirm stereotypes as a major source of gender bias, especially in female contexts, while observing bias even in language combinations traditionally less examined.