Aim: The use of artificial intelligence is set to be a paradigm shift in the way vascular surgery is practiced. In this study, we aimed to investigate the use of generic large language models for the planning of endovascular aneurysm repair (EVAR). Method: 21 patients who had been selected by a surgeon for a Cook standard infrarenal EVAR at Royal Hobart Hospital were identified between September 2021 to September 2025. A single surgeon’s measurements of main body, contralateral and ipsilateral limb diameters and lengths were collected through Cook planning worksheets. A ChatGPT-5 large language model was then used to predict EVAR component selection. The model was trained to oversize by ~10-20%, leaving enough space to cannulate the contralateral gate, and maximising overlap between main body and limbs. Measurements were inputted using a standardised dialogue and a plan was generated. The Cook worksheet plan and ChatGPT plan were compared to intra-operative graft sizes used, and feasibility of the ChatGPT plan was assessed by two investigators. Results were analysed using correlation matrices and Spearman coefficients were calculated. Results: All ChatGPT plans were feasible for overall anatomy although there were variations in main body and limb lengths. The ChatGPT plans demonstrated statistically significant correlation to the intra-operative graft sizes used. Main body diameter/length, contralateral limb diameter, and ipsilateral limb diameter/length showed high degrees of correlation (p<0.001). Contralateral limb length also correlated (p = 0.013). Conclusion: It is feasible to use a trained generic large language model for EVAR planning.