Classifying natural language requirements (NLRs) is challenging, especially with large volumes. Research shows that Large Language Models can assist by categorizing NLRs into functional requirements (FR) and non-functional requirements (NFRs). However, Generative Pretrained Transformer (GPT) models are not typically favored for this task due to concerns about consistency. This paper investigates the consistency when a GPT model classifies NLRs into FRs and NFRs using a zero-shot learning approach. Results show that ChatGPT-4o performs better for FRs, a temperature parameter set to 1 yields the highest consistency, while NFR classification improves with higher temperatures.