State-of-the-art Large Language Models (LLMs) demonstrate exceptional proficiency across diverse tasks, yet systematic evaluations of their linguistic abilities remain limited. This paper addresses this gap by proposing a new evaluation framework leveraging the potentialities of Controllable Text Generation. Our approach evaluates the models’ capacity to generate sentences that adhere to specific linguistic constraints and their ability to recognize the linguistic properties of their own generated sentences, also in terms of consistency with the specified constraints. We tested our approach on six Italian LLMs using various linguistic constraints.