Topic modelling revealed further differences in the interpretability of annotations between the two models, especially when understanding video content from aggregated descriptions of individual frames. For example, both <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> and LLaVA produced an ‘intertitle’ topic. However, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> was more specific and detailed, identifying the actual logo of the YouTube channel (“current time” [настоящее время]) with keywords such as ‘black background’, ‘white text’, ‘Russian language’, ‘Cyrillic script’, ‘logo’, ‘настоящее время logo’ or ‘political content’’. In contrast, LLaVA produced more common and less diverse keywords, such as ‘Russian’, ‘text’, ‘foreign language’, ‘black’ or ‘foreign’, and also contained errors (e.g. by identifying the Russian text as ‘Bulgarian’). While the keywords produced by <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> were more concise and allowed a better interpretation of the topic, the disadvantage of LLaVA was somewhat mitigated when we applied the topic model to the whole documentary. Both ‘intertitle’ topics allowed us to immediately identify the intertitles in the documentary and, thus, served their purpose well (see Figure 1).