We explore the use of Large Language Models (LLMs) for Discrete Event Simulation (DES). While DES typically involves both domain and technical expertise, our study demonstrates the potential of LLMs in generating queueing models in Python. The code outputs generated by the LLMs are compared to solutions implemented in GPSS (General Purpose Simulation System), a simulation language for DES. Prompt engineering is also reviewed, showcasing its impact on the quality of code generated by LLMs. Our results show that while LLMs assist in speeding up DES, they are far from replacing human experts. However, considering the steady advancements in Artificial Intelligence (AI), there is a promising future for more sophisticated and capable models.