Large language models (LLMs) rely heavily on high-quality training data, yet human-generated corpora face increasing scarcity due to legal and practical constraints. Synthetic data generated by LLMs is emerging as a scalable alternative; however, concerns remain about its linguistic quality and diversity. While previous research has identified potential degradation in English synthetic corpora, the effects in Spanish, a language with distinct grammatical characteristics, remain underexplored. This research proposal aims to conduct a systematic linguistic evaluation of synthetic Spanish corpora generated by state-of-the-art LLMs, comparing them with human-written texts. The study will analyse three key dimensions: lexical, syntactic, and semantic diversity, using established corpus linguistics metrics. Through this comparative framework, the proposal intends to identify potential linguistic simplifications and degradation patterns in synthetic Spanish data. Ultimately, the proposed outcome is expected to contribute valuable insights to support the creation of robust and reliable Natural Language Processing (NLP) models for Spanish.