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Video understanding

Note: This version of the page covers the new Interactions API, which is currently in Beta.
For stable production deployments, we recommend you continue to use the generateContent API. You can use the toggle on this page to switch between the versions.

To learn about video generation, see the Veo guide.

Gemini models can process videos, enabling many frontier developer use cases that would have historically required domain specific models. Some of Gemini's vision capabilities include the ability to: describe, segment, and extract information from videos, answer questions about video content, and refer to specific timestamps within a video.

You can provide videos as input to Gemini in the following ways:

Input method	Max size	Recommended use case
File API	20GB (paid) / 2GB (free)	Large files (100MB+), long videos (10min+), reusable files.
Cloud Storage Registration	2GB (per file, no storage limits)	Large files (100MB+), long videos (10min+), persistent, reusable files.
Inline Data	< 100MB	Small files (<100MB), short duration (<1min), one-off inputs.
YouTube URLs	N/A	Public YouTube videos.

Note: The File API is recommended for most use cases, especially for files larger than 100MB or when you want to reuse the file across multiple requests.

To learn about other file input methods, such as using external URLs or files stored in Google Cloud, see the File input methods guide.

Upload a video file

The following code downloads a sample video, uploads it using the Files API, waits for it to be processed, and then uses the uploaded file reference to summarize the video.

Python

from google import genai
import base64

client = genai.Client()

myfile = client.files.upload(file="path/to/sample.mp4")

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=[
        {"type": "video", "uri": myfile.uri, "mime_type": myfile.mime_type},
        {"type": "text", "text": "Summarize this video. Then create a quiz with an answer key based on the information in this video."}
    ]
)

print(interaction.steps[-1].content[0].text)

JavaScript

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI({});

async function main() {
  const myfile = await ai.files.upload({
    file: "path/to/sample.mp4",
    config: { mimeType: "video/mp4" },
  });

  const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: [
      createPartFromUri(myfile.uri, myfile.mimeType),
      "Summarize this video. Then create a quiz with an answer key based on the information in this video.",
    ],
  });
  console.log(interaction.steps.at(-1).content[0].text);
}

await main();

REST

VIDEO_PATH="path/to/sample.mp4"
MIME_TYPE=$(file -b --mime-type "${VIDEO_PATH}")
NUM_BYTES=$(wc -c < "${VIDEO_PATH}")
DISPLAY_NAME=VIDEO

tmp_header_file=upload-header.tmp

echo "Starting file upload..."
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -D ${tmp_header_file} \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

echo "Uploading video data..."
curl "${upload_url}" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${VIDEO_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri

echo "File uploaded successfully. File URI: ${file_uri}"

echo "Generating content from video..."
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "video", "uri": "'${file_uri}'", "mime_type": "'${MIME_TYPE}'"},
        {"type": "text", "text": "Summarize this video. Then create a quiz with an answer key based on the information in this video."}
      ]
    }' 2> /dev/null > response.json

jq ".steps[].content[0].text" response.json

Always use the Files API when the total request size (including the file, text prompt, system instructions, etc.) is larger than 20 MB, the video duration is significant, or if you intend to use the same video in multiple prompts. The File API accepts video file formats directly.

To learn more about working with media files, see Files API.

Pass video data inline

Instead of uploading a video file using the File API, you can pass smaller videos directly in the request. This is suitable for shorter videos under 20MB total request size.

Here's an example of providing inline video data:

Python

from google import genai

video_file_name = "/path/to/your/video.mp4"
video_bytes = open(video_file_name, 'rb').read()

client = genai.Client()
interaction = client.interactions.create(
    model='gemini-3-flash-preview',
    input=[
        {"type": "text", "text": "Please summarize the video in 3 sentences."},
        {
            "type": "video",
            "data": base64.b64encode(video_bytes).decode('utf-8'),
            "mime_type": "video/mp4"
        }
    ]
)
print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const ai = new GoogleGenAI({});
const base64VideoFile = fs.readFileSync("path/to/small-sample.mp4", {
  encoding: "base64",
});

const interaction = await ai.interactions.create({
  model: "gemini-3-flash-preview",
  input: [
    { type: "text", text: "Please summarize the video in 3 sentences." },
    {
      type: "video",
      data: base64VideoFile,
      mime_type: "video/mp4",
    }
  ],
});
console.log(interaction.steps.at(-1).content[0].text);

REST

VIDEO_PATH=/path/to/your/video.mp4

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "text", "text": "Please summarize the video in 3 sentences."},
        {
          "type": "video",
          "data": "'$(base64 $B64FLAGS $VIDEO_PATH)'",
          "mime_type": "video/mp4"
        }
      ]
    }' 2> /dev/null

Pass YouTube URLs

You can pass YouTube URLs directly to Gemini API as part of your request as follows:

Python

from google import genai

client = genai.Client()
interaction = client.interactions.create(
    model='gemini-3-flash-preview',
    input=[
        {"type": "text", "text": "Please summarize the video in 3 sentences."},
        {
            "type": "video",
            "uri": "https://www.youtube.com/watch?v=9hE5-98ZeCg"
        }
    ]
)
print(interaction.steps[-1].content[0].text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({});

const interaction = await ai.interactions.create({
  model: "gemini-3-flash-preview",
  input: [
    { type: "text", text: "Please summarize the video in 3 sentences." },
    {
      type: "video",
      uri: "https://www.youtube.com/watch?v=9hE5-98ZeCg",
    }
  ],
});
console.log(interaction.steps.at(-1).content[0].text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": [
        {"type": "text", "text": "Please summarize the video in 3 sentences."},
        {
          "type": "video",
          "uri": "https://www.youtube.com/watch?v=9hE5-98ZeCg"
        }
      ]
    }' 2> /dev/null

Limitations:

For the free tier, you can't upload more than 8 hours of YouTube video per day.
For the paid tier, there is no limit based on video length.
For models prior to Gemini 2.5, you can upload only 1 video per request. For Gemini 2.5 and later models, you can upload a maximum of 10 videos per request.
You can only upload public videos (not private or unlisted videos).

Refer to timestamps in the content

You can ask questions about specific points in time within the video using timestamps of the form MM:SS.

Python

prompt = "What are the examples given at 00:05 and 00:10 supposed to show us?"

JavaScript

const prompt = "What are the examples given at 00:05 and 00:10 supposed to show us?";

REST

PROMPT="What are the examples given at 00:05 and 00:10 supposed to show us?"

Extract detailed insights from video

Gemini models offer powerful capabilities for understanding video content by processing information from both the audio and visual streams. This lets you extract a rich set of details, including generating descriptions of what is happening in a video and answering questions about its content.

For visual descriptions, the model samples the video at a rate of 1 frame per second (FPS). This default sampling rate works well for most content, but note that it may miss details in videos with rapid motion or quick scene changes. For such high-motion content, consider setting a custom frame rate.

Python

prompt = "Describe the key events in this video, providing both audio and visual details. Include timestamps for salient moments."

JavaScript

const prompt = "Describe the key events in this video, providing both audio and visual details. Include timestamps for salient moments.";

REST

PROMPT="Describe the key events in this video, providing both audio and visual details. Include timestamps for salient moments."

Supported video formats

Gemini supports the following video format MIME types:

video/mp4
video/mpeg
video/mov
video/avi
video/x-flv
video/mpg
video/webm
video/wmv
video/3gpp

Technical details about videos

Supported models & context: All Gemini can process video data.
- Models with a 1M context window can process videos up to 1 hour long at default media resolution or 3 hours long at low media resolution.
File API processing: When using the File API, videos are stored at 1 frame per second (FPS) and audio is processed at 1Kbps (single channel). Timestamps are added every second.
- These rates are subject to change in the future for improvements in inference.
Token calculation: Each second of video is tokenized as follows:
- Individual frames (sampled at 1 FPS):
  - If media_resolution is set to low, frames are tokenized at 66 tokens per frame.
  - Otherwise, frames are tokenized at 258 tokens per frame.
- Audio: 32 tokens per second.
- Metadata is also included.
- Total: Approximately 300 tokens per second of video at default media resolution, or 100 tokens per second of video at low media resolution.
Medial resolution: Gemini 3 introduces granular control over multimodal vision processing with the media_resolution parameter. The media_resolution parameter determines the maximum number of tokens allocated per input image or video frame. Higher resolutions improve the model's ability to read fine text or identify small details, but increase token usage and latency.

calculations, see the tokens guide.
Timestamp format: When referring to specific moments in a video within your prompt, use the MM:SS format (e.g., 01:15 for 1 minute and 15 seconds).
Best practices:
- Use only one video per prompt request for optimal results.
- If combining text and a single video, place the text prompt after the video part in the input array.
- Be aware that fast action sequences might lose detail due to the 1 FPS sampling rate. Consider slowing down such clips if necessary.

What's next

This guide shows how to upload video files and generate text outputs from video inputs. To learn more, see the following resources:

System instructions: System instructions let you steer the behavior of the model based on your specific needs and use cases.
Files API: Learn more about uploading and managing files for use with Gemini.
File prompting strategies: The Gemini API supports prompting with text, image, audio, and video data, also known as multimodal prompting.
Safety guidance: Sometimes generative AI models produce unexpected outputs, such as outputs that are inaccurate, biased, or offensive. Post-processing and human evaluation are essential to limit the risk of harm from such outputs.