Gemini 3 can handle video reasoning by treating the video as a sequence of visual and sometimes audio events along a timeline. When you provide video input, the model receives representations of frames and, in some configurations, associated text like subtitles or transcripts. It can then reason about the order of events, identify key moments, and link actions to times or segments. You can ask questions like “What happens after the user clicks the red button?” or “Summarize the steps of this procedure in order,” and the model responds based on the temporal structure of the video.
To get good results, you should ask timeline-aware questions. Instead of “What is in this video?”, try “Describe the video in three phases: beginning, middle, end” or “List the main actions in chronological order.” If your video has audio narration or on-screen text, it helps to mention that: “Consider both the visuals and the spoken narration.” For debugging or documentation use cases, you might request a step-by-step breakdown: “Generate a numbered list of actions the presenter takes, in order.”
Video reasoning becomes especially useful when combined with other data sources. For example, imagine a training system where each video is indexed by topic in a vector database such asMilvus or Zilliz Cloud.. A user might ask “Show me all videos that explain error handling, and then summarize the common pattern.” You retrieve the relevant video segments, ask Gemini 3 to reason across them, and generate a consolidated, timeline-aware explanation. Similarly, for UI testing or demo videos, Gemini 3 can look at the sequence of screens and actions, detect inconsistencies, and describe the flow in terms a developer or product manager can act on.