Gemini 3 Pro & Flash, Gemini 3 Pro Image (nano banana pro), and the latest Gemini Live API native audio models are now available to use with Firebase AI Logic on all platforms!

Analyze video files using the Gemini API

You can ask a Gemini model to analyze video files that you provide either inline (base64-encoded) or via URL. When you use Firebase AI Logic, you can make this request directly from your app.

With this capability, you can do things like:

Caption and answer questions about videos
Analyze specific segments of a video using timestamps
Transcribe video content by processing both the audio track and visual frames
Describe, segment, and extract information from videos, including both the audio track and visual frames

Jump to code samples Jump to code for streamed responses

See other guides for additional options for working with video
Generate structured output Multi-turn chat

Before you begin

Click your Gemini API provider to view provider-specific content and code on this page.

If you haven't already, complete the getting started guide, which describes how to set up your Firebase project, connect your app to Firebase, add the SDK, initialize the backend service for your chosen Gemini API provider, and create a GenerativeModel instance.

For testing and iterating on your prompts, we recommend using Google AI Studio.

Need a sample video file?

You can use this publicly available file with a MIME type of video/mp4 (view or download file). https://storage.googleapis.com/cloud-samples-data/video/animals.mp4

Generate text from video files (base64-encoded)

Before trying this sample, complete the Before you begin section of this guide to set up your project and app.
In that section, you'll also click a button for your chosen Gemini API provider so that you see provider-specific content on this page.

You can ask a Gemini model to generate text by prompting with text and video—providing each input file's mimeType and the file itself. Find requirements and recommendations for input files later on this page.

Note that this example shows providing the file inline, but the SDKs also support providing a YouTube URL.

Swift

You can call generateContent() to generate text from multimodal input of text and video files.

 import FirebaseAILogic // Initialize the Gemini Developer API backend service let ai = FirebaseAI.firebaseAI(backend: .googleAI()) // Create a `GenerativeModel` instance with a model that supports your use case let model = ai.generativeModel(modelName: "gemini-2.5-flash") // Provide the video as `Data` with the appropriate MIME type. let video = InlineDataPart(data: try Data(contentsOf: videoURL), mimeType: "video/mp4") // Provide a text prompt to include with the video let prompt = "What is in the video?" // To generate text output, call generateContent with the text and video let response = try await model.generateContent(video, prompt) print(response.text ?? "No text in response.")

Kotlin

You can call generateContent() to generate text from multimodal input of text and video files.

^{For Kotlin, the methods in this SDK are suspend functions and need to be called from a Coroutine scope.}

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case val model = Firebase.ai(backend = GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash") val contentResolver = applicationContext.contentResolver contentResolver.openInputStream(videoUri).use { stream ->  stream?.let {  val bytes = stream.readBytes()  // Provide a prompt that includes the video specified above and text  val prompt = content {  inlineData(bytes, "video/mp4")  text("What is in the video?")  }  // To generate text output, call generateContent with the prompt  val response = model.generateContent(prompt)  Log.d(TAG, response.text ?: "")  } }

Java

You can call generateContent() to generate text from multimodal input of text and video files.

^{For Java, the methods in this SDK return a ListenableFuture.}

 // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case GenerativeModel ai = FirebaseAI.getInstance(GenerativeBackend.googleAI())  .generativeModel("gemini-2.5-flash"); // Use the GenerativeModelFutures Java compatibility layer which offers // support for ListenableFuture and Publisher APIs GenerativeModelFutures model = GenerativeModelFutures.from(ai); ContentResolver resolver = getApplicationContext().getContentResolver(); try (InputStream stream = resolver.openInputStream(videoUri)) {  File videoFile = new File(new URI(videoUri.toString()));  int videoSize = (int) videoFile.length();  byte[] videoBytes = new byte[videoSize];  if (stream != null) {  stream.read(videoBytes, 0, videoBytes.length);  stream.close();  // Provide a prompt that includes the video specified above and text  Content prompt = new Content.Builder()  .addInlineData(videoBytes, "video/mp4")  .addText("What is in the video?")  .build();  // To generate text output, call generateContent with the prompt  ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);  Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {  @Override  public void onSuccess(GenerateContentResponse result) {  String resultText = result.getText();  System.out.println(resultText);  }  @Override  public void onFailure(Throwable t) {  t.printStackTrace();  }  }, executor);  } } catch (IOException e) {  e.printStackTrace(); } catch (URISyntaxException e) {  e.printStackTrace(); }

Web

You can call generateContent() to generate text from multimodal input of text and video files.

 import { initializeApp } from "firebase/app"; import { getAI, getGenerativeModel, GoogleAIBackend } from "firebase/ai"; // TODO(developer) Replace the following with your app's Firebase configuration // See: https://firebase.google.com/docs/web/learn-more#config-object const firebaseConfig = {  // ... }; // Initialize FirebaseApp const firebaseApp = initializeApp(firebaseConfig); // Initialize the Gemini Developer API backend service const ai = getAI(firebaseApp, { backend: new GoogleAIBackend() }); // Create a `GenerativeModel` instance with a model that supports your use case const model = getGenerativeModel(ai, { model: "gemini-2.5-flash" }); // Converts a File object to a Part object. async function fileToGenerativePart(file) {  const base64EncodedDataPromise = new Promise((resolve) => {  const reader = new FileReader();  reader.onloadend = () => resolve(reader.result.split(',')[1]);  reader.readAsDataURL(file);  });  return {  inlineData: { data: await base64EncodedDataPromise, mimeType: file.type },  }; } async function run() {  // Provide a text prompt to include with the video  const prompt = "What do you see?";  const fileInputEl = document.querySelector("input[type=file]");  const videoPart = await fileToGenerativePart(fileInputEl.files[0]);  // To generate text output, call generateContent with the text and video  const result = await model.generateContent([prompt, videoPart]);  const response = result.response;  const text = response.text();  console.log(text); } run();

Dart

You can call generateContent() to generate text from multimodal input of text and video files.

 import 'package:firebase_ai/firebase_ai.dart'; import 'package:firebase_core/firebase_core.dart'; import 'firebase_options.dart'; // Initialize FirebaseApp await Firebase.initializeApp(  options: DefaultFirebaseOptions.currentPlatform, ); // Initialize the Gemini Developer API backend service // Create a `GenerativeModel` instance with a model that supports your use case final model =  FirebaseAI.googleAI().generativeModel(model: 'gemini-2.5-flash'); // Provide a text prompt to include with the video final prompt = TextPart("What's in the video?"); // Prepare video for input final video = await File('video0.mp4').readAsBytes(); // Provide the video as `Data` with the appropriate mimetype final videoPart = InlineDataPart('video/mp4', video); // To generate text output, call generateContent with the text and images final response = await model.generateContent([  Content.multi([prompt, ...videoPart]) ]); print(response.text);

Unity

You can call GenerateContentAsync() to generate text from multimodal input of text and video files.

 using Firebase; using Firebase.AI; // Initialize the Gemini Developer API backend service var ai = FirebaseAI.GetInstance(FirebaseAI.Backend.GoogleAI()); // Create a `GenerativeModel` instance with a model that supports your use case var model = ai.GetGenerativeModel(modelName: "gemini-2.5-flash"); // Provide the video as `data` with the appropriate MIME type. var video = ModelContent.InlineData("video/mp4",  System.IO.File.ReadAllBytes(System.IO.Path.Combine(  UnityEngine.Application.streamingAssetsPath, "yourVideo.mp4"))); // Provide a text prompt to include with the video var prompt = ModelContent.Text("What is in the video?"); // To generate text output, call GenerateContentAsync with the text and video var response = await model.GenerateContentAsync(new [] { video, prompt }); UnityEngine.Debug.Log(response.Text ?? "No text in response.");

Learn how to choose a model appropriate for your use case and app.

Stream the response

You can achieve faster interactions by not waiting for the entire result from the model generation, and instead use streaming to handle partial results. To stream the response, call generateContentStream.