This study investigates the effectiveness of Large Language Models (LLMs) in simplifying Italian administrative texts compared to human informants. This research evaluates the performance of several well-known LLMs, including GPT-3.5-Turbo, GPT-4, LLaMA 3, and Phi 3, in simplifying a corpus of Italian administrative documents (s-ItaIst), a representative corpus of Italian administrative texts. To accurately compare the simplification abilities of humans and LLMs, six parallel corpora of a subsection of ItaIst are collected. These parallel corpora were analyzed using both complexity and similarity metrics to assess the outcomes of LLMs and human participants. Our findings indicate that while LLMs perform comparably to humans in many aspects, there are notable differences in structural and semantic changes. The results of our study underscore the potential and limitations of using AI for administrative text simplification, highlighting areas where LLMs need improvement to achieve human-level proficiency.