This study evaluates the ability of large language models (LLMs) to detect incoherence between the text of product reviews and their assigned rating (1 or 5 stars). Using popular LLMs such as GPT-5, Llama-4 and DeepSeek-3.2, and models optimized for Brazilian Portuguese, Sabiá-3.1 and Bode-3.1, we show that some are capable of detecting incoherence among texts and ratings (F1 > 90%) in a zero-shot protocol. Models also present a high agreement in the predictions, where several prediction rounds led to low variability (Fleiss’ κ> 0.95). With the demonstrated incoherence present in all product categories (aprox. 10% of comments), the results suggest that LLMs are very promising to perform this high semantic interpretation task, and they can be used as valuable tools for online monitoring and recommendation systems.