In an era of pervasive misinformation, the help of artificial intelligence like Large Language Models (LLMs) in identifying and correcting falsehoods is critical. This study investigates the alignment between humans’ and GPT-4’s judgments on lying and falsity, with a focus on deceptive implicatures—statements that are literally true but imply something false. Using a cross-cultural human dataset (3660 participants across ten countries) as a benchmark, we evaluated GPT-4’s performance in six languages. Results indicate that while GPT-4’s judgments correlate significantly with human responses, notable discrepancies also exist. GPT-4 tends to attribute higher lie scores and lower falsity scores than humans and exhibits more severe moral judgments. These findings highlight the potential risks of deploying LLMs like GPT-4 for misinformation detection, suggesting possible over- or under classification (depending on the task). The study highlights the importance of cautious implementation and the need for further refinement to enhance human-LLM alignment in identifying deception.