While multilingual large language models (LLMs) perform well on high-level tasks like translation and question answering, their ability to handle grammatical gender and morphological agreement remains underexplored.In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions to gender.We thus introduce MORPHOGEN a morphologically grounded largescale benchmark dataset for evaluating genderaware generation in three typologically diverse grammatically gendered languages i.e.French, Arabic and Hindi.The core task, GENFORM, requires models to rewrite a first-person sentence in the opposite gender while preserving its meaning and structure.We construct a highquality synthetic dataset spanning French, Arabic, and Hindi, and benchmark 15 popular multilingual LLMs (2B-70B) on their ability to perform this transformation.Our results reveal gaps and interesting insights into the handling of morphological gender in current models.MORPHOGEN offers a focused diagnostic lens for gender-aware language modeling and lays the groundwork for future research on inclusive and morphology-sensitive NLP.