This study investigates gender bias in the Dutch RobBERT-2023 language model using an adapted version of the HONEST framework, which assesses harmful sentence completions. By translating and expanding HONEST templates to include non-binary and gender-neutral language, we systematically evaluate whether RobBERT-2023 exhibits biased or harmful outputs across gender identities. Our findings reveal that while the model’s overall bias score is relatively low, non-binary identities are disproportionately affected by derogatory language.