Artificial intelligence (AI) has transformed numerous fields, including linguistics. Large Language Models (LLMs) have revolutionized interaction with text by providing responses that mimic human language. These models not only generate text, but also reflect their interpretation of the world. However, these models’ understanding of the world is limited, which has led to the the proposal of developing Large World Models (LWMs), which integrate textual, visual, and auditory data for a more complete understanding. This article employs a lexicostatistical perspective to analyze how LLMs articulate responses based on their world models. A comparative quasi-experimental design was utilized to evaluate six different LLMs. The methodology focused on measuring the diversity and lexical density of the texts generated by these models. The results demonstrated that ChatGPT-4 has high lexical density and moderate lexical diversity, while Copilot has the highest lexical diversity but lower lexical density. This analysis is of great importance for understanding the capabilities and limitations of LLMs, with implications for their applications in various areas. The concepts, and methodology are presented, the findings are discussed, and the paper concludes with reflections on future research and practical applications.