That rather depends on exactly how this is done, although it's a useful upper bound for many tasks either way. You could say the same for images and yet due to the way some work they straight up
cannot see in certain ways.
This could describe adding a frame of nonsense into an existing video.
It also could describe finding a semantically useful thing in an actual video, where the exact location is randomised by looking at different time crops of the video. For example, finding a book on a desk in a video that's only there in a panning shot, and you then see if it can find it in a 10s cut, 20s cut, 10 minute cut, etc, and near the start/middle/end.
Here's the paper: https://arxiv.org/pdf/2511.21631
> To evaluate the model’s capability in processing long-context inputs, we construct a video “Needle-ina-Haystack” evaluation on Qwen3-VL-235B-A22B-Instruct. In this task, a semantically salient “needle”
frame—containing critical visual evidence—is inserted at varying temporal positions within a long video.
The model is then tasked with accurately locating the target frame from the long video and answering the
corresponding question. During evaluation, videos are uniformly sampled at 1 FPS, and frame resolution
is dynamically adjusted to maintain a constant visual token budget.
This potentially sounds more like the former, but I can't find more accurate information on how this works.
Regardless I'd say again that while not the whole story things like this really are useful to know, and can be very important to test - it's really not a given that models can always find anything in their context window, perhaps even more so for video.