You can ask a Gemini model to analyze audio 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 mobile or web app (such as your Apple (Swift), Android (Kotlin / Java), Web (JavaScript), Flutter (Dart), or Unity app). With this capability, you can do things like: - Describe, summarize, or answer questions about audio content - Transcribe audio content - Analyze specific segments of audio using timestamps This guide is about generating *text* from audio input. [Jump to code samples](https://firebase.google.com/docs/ai-logic/analyze-audio.md.txt#base64) [Jump to code for streamed responses](https://firebase.google.com/docs/ai-logic/analyze-audio.md.txt#streaming)
|---| | **See other guides for additional options for working with audio** [Generate structured output](https://firebase.google.com/docs/ai-logic/generate-structured-output.md.txt) [Multi-turn chat](https://firebase.google.com/docs/ai-logic/chat.md.txt) [Text-to-speech (TTS)](https://firebase.google.com/docs/ai-logic/generate-speech.md.txt) [Bidirectional streaming](https://firebase.google.com/docs/ai-logic/live-api.md.txt) | ## 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](https://firebase.google.com/docs/ai-logic/get-started.md.txt), 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. > [!NOTE] > All our docs assume that you're using the [latest versions](https://firebase.google.com/support/releases.md.txt) of the Firebase AI Logic SDKs.
For testing and iterating on your prompts, we recommend using [Google AI Studio](https://aistudio.google.com).
Need a sample audio file?
> You can use this publicly available file with a MIME type of `audio/mp3` > ([view or download file](https://storage.googleapis.com/cloud-samples-data/generative-ai/audio/pixel.mp3)). > `https://storage.googleapis.com/cloud-samples-data/generative-ai/audio/pixel.mp3`

#### Models that support this capability This guide is about generating text from audio input, and it's applicable to the following Gemini models: - `gemini-4-carbon` - `gemini-4-argon` - `gemini-3.8-flash` - `gemini-3.5-flash-lite` (and the older `gemini-3.1-flash-lite`) Older general-use Gemini 3.x Flash models support this capability, but they're no longer recommended for use due to upcoming shutdown dates and/or automatic routing to newer models. ## Generate text from audio files (base64-encoded) |---| | *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/analyze-audio.md.txt#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 audio---providing the input file's `mimeType` and the file itself. Find [requirements and recommendations for input files](https://firebase.google.com/docs/ai-logic/analyze-audio.md.txt#requirements-recommendations-for-input) later on this page. > [!NOTE] > **Important** : **The total request size limit is > 20 MB.** To send large files, review the [options for providing files in multimodal requests](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt).
### Swift You can call [`generateContent()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontent_:) to generate text from multimodal input of text and a single audio file. ```swift 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-3.8-flash") // Provide the audio as \`Data\` guard let audioData = try? Data(contentsOf: audioURL) else { print("Error loading audio data.") return // Or handle the error appropriately } // Specify the appropriate audio MIME type let audio = InlineDataPart(data: audioData, mimeType: "audio/mpeg") // Provide a text prompt to include with the audio let prompt = "Transcribe what's said in this audio recording." // To generate text output, call \`generateContent\` with the audio and text prompt let response = try await model.generateContent(audio, prompt) // Print the generated text, handling the case where it might be nil print(response.text ?? "No text in response.") ``` ### Kotlin You can call [`generateContent()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.Array)) to generate text from multimodal input of text and a single audio file. ^*For Kotlin, the methods in this SDK are suspend functions and need to be called from a [Coroutine scope](https://developer.android.com/kotlin/coroutines).*^ ```kotlin // 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-3.8-flash") val contentResolver = applicationContext.contentResolver val inputStream = contentResolver.openInputStream(audioUri) if (inputStream != null) { // Check if the audio loaded successfully inputStream.use { stream -\> val bytes = stream.readBytes() // Provide a prompt that includes the audio specified above and text val prompt = content { inlineData(bytes, "audio/mpeg") // Specify the appropriate audio MIME type text("Transcribe what's said in this audio recording.") } // To generate text output, call \`generateContent\` with the prompt val response = model.generateContent(prompt) // Log the generated text, handling the case where it might be null Log.d(TAG, response.text?: "") } } else { Log.e(TAG, "Error getting input stream for audio.") // Handle the error appropriately } ``` ### Java You can call [`generateContent()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContent(kotlin.Array)) to generate text from multimodal input of text and a single audio file. ^*For Java, the methods in this SDK return a [`ListenableFuture`](https://developer.android.com/guide/background/asynchronous/listenablefuture).*^ ```java // 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-3.8-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(audioUri)) { File audioFile = new File(new URI(audioUri.toString())); int audioSize = (int) audioFile.length(); byte audioBytes = new byte\[audioSize\]; if (stream != null) { stream.read(audioBytes, 0, audioBytes.length); stream.close(); // Provide a prompt that includes the audio specified above and text Content prompt = new Content.Builder() .addInlineData(audioBytes, "audio/mpeg") // Specify the appropriate audio MIME type .addText("Transcribe what's said in this audio recording.") .build(); // To generate text output, call \`generateContent\` with the prompt ListenableFuture\ response = model.generateContent(prompt); Futures.addCallback(response, new FutureCallback\() { @Override public void onSuccess(GenerateContentResponse result) { String text = result.getText(); Log.d(TAG, (text == null) ? "" : text); } @Override public void onFailure(Throwable t) { Log.e(TAG, "Failed to generate a response", t); } }, executor); } else { Log.e(TAG, "Error getting input stream for file."); // Handle the error appropriately } } catch (IOException e) { Log.e(TAG, "Failed to read the audio file", e); } catch (URISyntaxException e) { Log.e(TAG, "Invalid audio file", e); } ``` ### Web You can call [`generateContent()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontent) to generate text from multimodal input of text and a single audio file. ```javascript 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-3.8-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(',')); reader.readAsDataURL(file); }); return { type: "inlineData", inlineData: { data: await base64EncodedDataPromise, mimeType: file.type }, }; } async function run() { // Provide a text prompt to include with the audio const prompt = "Transcribe what's said in this audio recording."; // Prepare audio for input const fileInputEl = document.querySelector("input\[type=file\]"); const audioPart = await fileToGenerativePart(fileInputEl.files); // To generate text output, call \`generateContent\` with the text and audio const result = await model.generateContent(\[prompt, audioPart\]); // Log the generated text, handling the case where it might be undefined console.log(result.response.text() ?? "No text in response."); } run(); ``` ### Dart You can call [`generateContent()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContent.html) to generate text from multimodal input of text and a single audio file. ```dart 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-3.8-flash'); // Provide a text prompt to include with the audio final prompt = TextPart("Transcribe what's said in this audio recording."); // Prepare audio for input final audio = await File('audio0.mp3').readAsBytes(); // Provide the audio as \`Data\` with the appropriate audio MIME type final audioPart = InlineDataPart('audio/mpeg', audio); // To generate text output, call \`generateContent\` with the text and audio final response = await model.generateContent(\[ Content.multi(\[prompt,audioPart\]) \]); // Print the generated text print(response.text); ``` ### Unity You can call [`GenerateContentAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentasync) to generate text from multimodal input of text and a single audio file. ```c# 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-3.8-flash"); // Provide a text prompt to include with the audio var prompt = ModelContent.Text("Transcribe what's said in this audio recording."); // Provide the audio as \`data\` with the appropriate audio MIME type var audio = ModelContent.InlineData("audio/mpeg", System.IO.File.ReadAllBytes(System.IO.Path.Combine( UnityEngine.Application.streamingAssetsPath, "audio0.mp3"))); // To generate text output, call \`GenerateContentAsync\` with the text and audio var response = await model.GenerateContentAsync(new \[\] { prompt, audio }); // Print the generated text UnityEngine.Debug.Log(response.Text ?? "No text in response."); ``` Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt) appropriate for your use case and app. ## Stream the response |---| | *Before trying this sample, complete the [Before you begin](https://firebase.google.com/docs/ai-logic/analyze-audio.md.txt#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 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`.
#### View example: Stream generated text from audio files
### Swift You can call [`generateContentStream()`](https://firebase.google.com/docs/reference/swift/firebaseailogic/api/reference/Classes/GenerativeModel.md.txt#generatecontentstream_:) to stream generated text from multimodal input of text and a single audio file. ```swift 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-3.8-flash") // Provide the audio as \`Data\` guard let audioData = try? Data(contentsOf: audioURL) else { print("Error loading audio data.") return // Or handle the error appropriately } // Specify the appropriate audio MIME type let audio = InlineDataPart(data: audioData, mimeType: "audio/mpeg") // Provide a text prompt to include with the audio let prompt = "Transcribe what's said in this audio recording." // To stream generated text output, call \`generateContentStream\` with the audio and text prompt let contentStream = try model.generateContentStream(audio, prompt) // Print the generated text, handling the case where it might be nil for try await chunk in contentStream { if let text = chunk.text { print(text) } } ``` ### Kotlin You can call [`generateContentStream()`](https://firebase.google.com/docs/reference/kotlin/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.Array)) to stream generated text from multimodal input of text and a single audio file. ^*For Kotlin, the methods in this SDK are suspend functions and need to be called from a [Coroutine scope](https://developer.android.com/kotlin/coroutines).*^ ```kotlin // 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-3.8-flash") val contentResolver = applicationContext.contentResolver val inputStream = contentResolver.openInputStream(audioUri) if (inputStream != null) { // Check if the audio loaded successfully inputStream.use { stream -\> val bytes = stream.readBytes() // Provide a prompt that includes the audio specified above and text val prompt = content { inlineData(bytes, "audio/mpeg") // Specify the appropriate audio MIME type text("Transcribe what's said in this audio recording.") } // To stream generated text output, call \`generateContentStream\` with the prompt var fullResponse = "" model.generateContentStream(prompt).collect { chunk -\> // Log the generated text, handling the case where it might be null Log.d(TAG, chunk.text?: "") fullResponse += chunk.text?: "" } } } else { Log.e(TAG, "Error getting input stream for audio.") // Handle the error appropriately } ``` ### Java You can call [`generateContentStream()`](https://firebase.google.com/docs/reference/android/com/google/firebase/ai/GenerativeModel.md.txt#generateContentStream(kotlin.Array)) to stream generated text from multimodal input of text and a single audio file. ^*For Java, the streaming methods in this SDK return a `Publisher` type from the [Reactive Streams library](https://www.reactive-streams.org/).*^ ```java // 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-3.8-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(audioUri)) { File audioFile = new File(new URI(audioUri.toString())); int audioSize = (int) audioFile.length(); byte audioBytes = new byte\[audioSize\]; if (stream != null) { stream.read(audioBytes, 0, audioBytes.length); stream.close(); // Provide a prompt that includes the audio specified above and text Content prompt = new Content.Builder() .addInlineData(audioBytes, "audio/mpeg") // Specify the appropriate audio MIME type .addText("Transcribe what's said in this audio recording.") .build(); // To stream generated text output, call \`generateContentStream\` with the prompt Publisher\ streamingResponse = model.generateContentStream(prompt); StringBuilder fullResponse = new StringBuilder(); streamingResponse.subscribe(new Subscriber\() { @Override public void onNext(GenerateContentResponse generateContentResponse) { String chunk = generateContentResponse.getText(); String text = (chunk == null) ? "" : chunk; Log.d(TAG, text); fullResponse.append(text); } @Override public void onComplete() { Log.d(TAG, fullResponse.toString()); } @Override public void onError(Throwable t) { Log.e(TAG, "Failed to generate a response", t); } @Override public void onSubscribe(Subscription s) { } }); } else { Log.e(TAG, "Error getting input stream for file."); // Handle the error appropriately } } catch (IOException e) { Log.e(TAG, "Failed to read the audio file", e); } catch (URISyntaxException e) { Log.e(TAG, "Invalid audio file", e); } ``` ### Web You can call [`generateContentStream()`](https://firebase.google.com/docs/reference/js/ai.generativemodel.md.txt#generativemodelgeneratecontentstream) to stream generated text from multimodal input of text and a single audio file. ```javascript 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-3.8-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(',')); reader.readAsDataURL(file); }); return { type: "inlineData", inlineData: { data: await base64EncodedDataPromise, mimeType: file.type }, }; } async function run() { // Provide a text prompt to include with the audio const prompt = "Transcribe what's said in this audio recording."; // Prepare audio for input const fileInputEl = document.querySelector("input\[type=file\]"); const audioPart = await fileToGenerativePart(fileInputEl.files); // To stream generated text output, call \`generateContentStream\` with the text and audio const result = await model.generateContentStream(\[prompt, audioPart\]); // Log the generated text for await (const chunk of result.stream) { const chunkText = chunk.text(); console.log(chunkText); } } run(); ``` ### Dart You can call [`generateContentStream()`](https://pub.dev/documentation/firebase_ai/latest/firebase_ai/GenerativeModel/generateContentStream.html) to stream generated text from multimodal input of text and a single audio file. ```dart 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-3.8-flash'); // Provide a text prompt to include with the audio final prompt = TextPart("Transcribe what's said in this audio recording."); // Prepare audio for input final audio = await File('audio0.mp3').readAsBytes(); // Provide the audio as \`Data\` with the appropriate audio MIME type final audioPart = InlineDataPart('audio/mpeg', audio); // To stream generated text output, call \`generateContentStream\` with the text and audio final response = await model.generateContentStream(\[ Content.multi(\[prompt, audioPart\]) \]); // Print the generated text await for (final chunk in response) { print(chunk.text); } ``` ### Unity You can call [`GenerateContentStreamAsync()`](https://firebase.google.com/docs/reference/unity/class/firebase/a-i/generative-model.md.txt#generatecontentstreamasync) to stream generated text from multimodal input of text and a single audio file. ```c# 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-3.8-flash"); // Provide a text prompt to include with the audio var prompt = ModelContent.Text("Transcribe what's said in this audio recording."); // Provide the audio as \`data\` with the appropriate audio MIME type var audio = ModelContent.InlineData("audio/mpeg", System.IO.File.ReadAllBytes(System.IO.Path.Combine( UnityEngine.Application.streamingAssetsPath, "audio0.mp3"))); // To stream generated text output, call \`GenerateContentStreamAsync\` with the text and audio var responseStream = model.GenerateContentStreamAsync(new \[\] { prompt, audio }); // Print the generated text await foreach (var response in responseStream) { if (!string.IsNullOrWhiteSpace(response.Text)) { UnityEngine.Debug.Log(response.Text); } } ``` Learn how to choose a [model](https://firebase.google.com/docs/ai-logic/models.md.txt) appropriate for your use case and app.


*** ** * ** *** ## Requirements and recommendations for input audio files > [!NOTE] > **Important** : **The total request size limit is > 20 MB.** To send large files, review the [options for providing files in multimodal requests](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt).
Note that a file provided as inline data is encoded to base64 in transit, which increases the size of the request. You get an HTTP 413 error if a request is too large. See "Supported input files and requirements" page to learn detailed information about the following: - [Different options for providing a file in a request](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt#options-for-input-files) (either inline or using the file's URL or URI) - [Requirements and best practices for audio files](https://firebase.google.com/docs/ai-logic/input-file-requirements.md.txt#audio) #### Supported audio MIME types Gemini multimodal models support the following audio MIME types: - AAC - `audio/aac` - FLAC - `audio/flac` - MP3 - `audio/mp3` - MPA - `audio/m4a` - MPEG - `audio/mpeg` - MPGA - `audio/mpga` - MP4 - `audio/mp4` - OPUS - `audio/opus` - PCM - `audio/pcm` - WAV - `audio/wav` - WEBM - `audio/webm`
#### Limits per request Maximum files per request: 1 audio file


*** ** * ** *** ## What else can you do? - Learn how to [count tokens](https://firebase.google.com/docs/ai-logic/count-tokens.md.txt) before sending long prompts to the model. - [Set up Cloud Storage for Firebase](https://firebase.google.com/docs/ai-logic/solutions/cloud-storage.md.txt) so that you can include large files in your multimodal requests and have a more managed solution for providing files in prompts. Files can include images, PDFs, video, and audio. - Start thinking about preparing for production (see the [production checklist](https://firebase.google.com/docs/ai-logic/production-checklist.md.txt)): - [Enforce Firebase App Check](https://firebase.google.com/docs/ai-logic/app-check.md.txt) *as early as possible* to help protect the Gemini API from abuse by unauthorized clients. - Use [Firebase Remote Config](https://firebase.google.com/docs/ai-logic/change-model-name-remotely.md.txt) or [server prompt templates](https://firebase.google.com/docs/ai-logic/server-prompt-templates/get-started.md.txt) so that you can make on-demand changes to configurations for your AI feature (like model name) without releasing a new version of your app. #### Try out other capabilities
- Build [multi-turn conversations (chat)](https://firebase.google.com/docs/ai-logic/chat.md.txt). - Generate text from [text-only prompts](https://firebase.google.com/docs/ai-logic/generate-text.md.txt). - [Generate structured output (like JSON)](https://firebase.google.com/docs/ai-logic/generate-structured-output.md.txt) from both text and multimodal prompts. - [Generate and edit images](https://firebase.google.com/docs/ai-logic/generate-images-gemini.md.txt) from both text and multimodal prompts. - [Generate speech (both single- and multiple-speakers)](https://firebase.google.com/docs/ai-logic/generate-speech) using Gemini text-to-speech (TTS) models. - [Stream input and output](https://firebase.google.com/docs/ai-logic/live-api.md.txt) (including audio) using the Gemini Live API. - Use tools (like [function calling](https://firebase.google.com/docs/ai-logic/function-calling.md.txt) and Grounding with [Google Search](https://firebase.google.com/docs/ai-logic/grounding-google-search.md.txt) or [Google Maps](https://firebase.google.com/docs/ai-logic/grounding-google-maps.md.txt)) to connect a Gemini model to other parts of your app and external systems and information. #### Learn how to control content generation - [Understand prompt design](https://firebase.google.com/docs/ai-logic/prompt-design.md.txt), including best practices, strategies, and example prompts. - [Configure model parameters](https://firebase.google.com/docs/ai-logic/model-parameters.md.txt) like max output tokens, the probability of repeated output tokens, etc. - [Use safety settings](https://firebase.google.com/docs/ai-logic/safety-settings.md.txt) to adjust the likelihood of getting responses that may be considered harmful. You can also experiment with prompts and model configurations and even get a generated code snippet using [Google AI Studio](https://aistudio.google.com).
#### Learn more about the supported models Learn about the [models available for various use cases](https://firebase.google.com/docs/ai-logic/models.md.txt) and their [quotas](https://firebase.google.com/docs/ai-logic/quotas.md.txt) and [pricing](https://firebase.google.com/docs/ai-logic/pricing.md.txt).

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