A first-look comparison of the annotations showcased clear differences in the keyword generation patterns of the two models. While <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> provided more consistent and typically more detailed annotations, LLaVA showed a higher variability in the results and a capacity for both very sparse and extremely wordy annotations. In terms of annotation quality, further human observations revealed more nuanced differences between the two models. <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> showed higher contextual accuracy, correctly identifying and including event-specific keywords such as 'Minsk', demonstrating its ability to accurately capture the implicit context of the images. In addition, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> excelled in OCR, particularly in recognising and correctly interpreting text within images, especially Cyrillic script. In contrast, LLaVA struggled with Cyrillic letters, often failing to recognise or accurately annotate the Russian or Belarusian text. Both models have difficulties with blurry night images due to poor lighting and low visibility.