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With the rise of generative AI, Large Language Models (LLMs) are repeatedly making a deep impression with their mind-blowing performances. They appear to be able to solve all kinds of tasks for which humans need a range of socio-cognitive abilities, such as reasoning, planning, and understanding. In humans, such abilities seem to be necessarily associated with consciousness. However, this does not rule out the possibility that there could be multiple realizations of such abilities that do not necessarily require consciousness. In view of the controversial debate about what properties and abilities we can ascribe to systems based on generative AI, I shall examine the question of the extent to which LLMs solve certain tasks in a very different way compared to the way humans solve such tasks and whether we might still be justified in ascribing agency and socio-cognitive abilities to them. To this end, I will discuss benchmarks and their appropriateness for drawing conclusions about socio-cognitive abilities or the way AI systems actually process information, addressing issues such as data contamination and robustness. Utilizing the distinction between “competence without comprehension” and “competence with comprehension”, and the idea that comprehension comes in degrees by Daniel Dennett, I will investigate whether there might be socio-cognitive abilities in artificial systems that could constitute something in between. Thereby, I shall investigate the potential range of multiple realizations of socio-cognitive abilities and the general difficulties concerning justified attribution of abilities and properties (including consciousness) to AIs.