This paper aims to investigate the presence of cognitive biases, more specifically of Availability heuristics, Representativeness heuristics and Framing, in OpenAI’s ChatGPT 3.5 and ChatGPT 4, as well as the linguistic dependency of their occurrences in the Large Language Models’ (LLMs) outputs. The innovative aspect of this research is conveyed by rephrasing three tasks proposed in Kahneman and Tversky’s works and determining whether the LLMs’ answers to the tasks are correct or incorrect and human-like or non-human-like. The latter classification is made possible by interviewing a total of 56 native speakers of Italian, English and Spanish, thus introducing a new linguistic comparison of results and forming a “human standard’. Our study indicates that GPTs 3.5 and 4 are very frequently subject to the cognitive biases under discussion and their answers are mostly non-human-like. There is minimal but significant discrepancy in the performance of GPT 3.5 and 4, slightly favouring ChatGPT 4 in avoiding biased responses, specifically for Availability heuristics. We also reveal that, while the results for ChatGPT 4 are not significantly language dependent, meaning that the performances in avoiding biases are not affected by the prompting language, their difference with ChatGPT 3.5 is statistically significant.