Google is adding agent-based video analysis to several Gemini models. Instead of scanning a video frame by frame at a fixed rate, the model hunts for relevant sections on its own, which Google says cuts token usage and costs by a wide margin.

The latest models, Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite, can pick up moments shorter than one second, including state changes or cuts that would slip through at one frame per second. Google says this makes automated video editing far more precise.

The system can also track down individual scenes in hours of footage without burning through millions of tokens. It spots anomalies by resampling suspicious time windows at a higher frame rate and accurately counts repeated movements and individual objects over time.

Until now, Gemini relied on static processing, sampling video at a fixed frame rate of one frame per second by default and adjustable through the API. Since native video analysis launched in 2025, Gemini has been transcribing the audio track and analyzing frames on a per-second basis.

Google says the agent-based variant ties the model’s reasoning directly to native video tools. The model decides on its own which sections to look at, at what speed, and through which modality, whether that means frames, audio, or transcript. It only pulls the moments and signals it actually needs for a given task.

Gemini now loads only the parts of the video that matter

Gemini now uses an internal tool to grab just the relevant portion of the video file. Developers could build this kind of selective approach manually before, but the model now handles it on its own.

The approach builds on “agentic vision,” which Google shipped for Gemini 3 Flash in January. That feature let the model write and run Python code to zoom, crop, and annotate images, checking each result in a think-act-observe loop before responding. It didn’t work automatically in every case at launch, but the groundwork was already there. When Google announced Gemini 3 Flash back in December, the company flagged visual and spatial reasoning for video as a coming capability.

The efficiency gains show up most with long videos, anywhere from 10-minute tutorials to 90-minute lectures and multi-hour recordings. With static processing, developers had to pick between high token costs and methods that throw away important details. On Google’s own benchmarks, including LongVideoBench, Gemini 3.7 Flash with agent-based analysis scores the highest overall quality and delivers the best mix of accuracy and cost efficiency.

No extra charge through the API

The feature is live for video uploads and YouTube videos through the Gemini API in Google AI Studio and on the Gemini Enterprise Agent Platform. Developers set the processing mode to “agentic” in the API config and pay standard Gemini API token rates with no added fee. More details are in the Developer Guide.

Google plans to bring these improvements to its own products too. The feature should roll out soon to all Gemini app users on Flash and Flash Lite devices. Over the coming months, agent-based video analysis will also power the “Ask YouTube” feature on the playback page, giving answers more closely tied to what’s actually visible in the video.