This paper explores the potential of large language models (LLMs) to automate gender-neutral translation (GNT) in response to the growing demand for inclusive language. Traditional machine translation (MT) systems are limited in their ability to generate GNT due to the lack of dedicated parallel data. The study compares the performance of MT models, including Amazon Translate and DeepL, with the popular GPT-4 model in translating English to Italian. Through extensive manual evaluations, the research reveals that while current MT systems struggle with GNT, GPT-4 shows promising neutralization capabilities with only a few examples. The study also highlights the subjectivity of judging the quality and acceptability of GNT, with moderate agreement among annotators. The findings suggest that LLMs can be a valuable solution for automating GNT, but further research is needed to address limitations such as the need for more diverse and realistic prompts and the variability in acceptability judgments. The paper concludes by discussing the ethical implications of GNT and the importance of non-binary language in fostering inclusivity.This paper explores the potential of large language models (LLMs) to automate gender-neutral translation (GNT) in response to the growing demand for inclusive language. Traditional machine translation (MT) systems are limited in their ability to generate GNT due to the lack of dedicated parallel data. The study compares the performance of MT models, including Amazon Translate and DeepL, with the popular GPT-4 model in translating English to Italian. Through extensive manual evaluations, the research reveals that while current MT systems struggle with GNT, GPT-4 shows promising neutralization capabilities with only a few examples. The study also highlights the subjectivity of judging the quality and acceptability of GNT, with moderate agreement among annotators. The findings suggest that LLMs can be a valuable solution for automating GNT, but further research is needed to address limitations such as the need for more diverse and realistic prompts and the variability in acceptability judgments. The paper concludes by discussing the ethical implications of GNT and the importance of non-binary language in fostering inclusivity.