Available capabilities when accessing the Gemini API through Apple's Foundation Models framework


The examples on this page assume that you've completed the Get started: Access the Gemini API through Apple's Foundation Models framework.


This guide shows you how to send various types of requests to the Gemini API through Apple's Foundation Models framework using the Firebase AI Logic SDK for Apple platforms.

This page shows examples for how to send the following types of requests:


Generate text

Gemini models support the following capabilities for generating text:

Models that support this capability

  • gemini-3.1-pro-preview
  • gemini-3.5-flash
  • gemini-3.1-flash-lite

Generate text from text-only input

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

You can ask a Gemini model to generate text by prompting with text-only input.

import FoundationModels
import FirebaseCore
import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Initialize a `geminiLanguageModel` with a Gemini model that supports your use case.
let model = ai.geminiLanguageModel(name: "gemini-3.5-flash")

// Provide a prompt that contains text.
let prompt = "Write a story about a magic backpack."

// Create a session by injecting the model into Apple's `LanguageModelSession`.
// For a single-turn interaction, create a new session each time you call the model.
let session = LanguageModelSession(model: model)

// Generate a text response to the prompt.
let response = try await session.respond(to: prompt)
print(response.content)

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, use streamResponse(to:) instead of respond(to:).

// imports
// initialization of Gemini API backend service and a `geminiLanguageModel`

// Provide a prompt that contains text.
let prompt = "Write a story about a magic backpack."

// Create a session by injecting the model into Apple's `LanguageModelSession`.
// For a single-turn interaction, create a new session each time you call the model.
let session = LanguageModelSession(model: model)

// Generate a text response to the prompt.
// To stream the response, use `streamResponse(to:)` instead of `respond(to:)`
let stream = session.streamResponse(to: "Write a story about a magic backpack.")
var response = ""
for try await snapshot in stream {
  // The snapshot contains *all* content generated so far.
  response = snapshot.content
}

Generate text during a multi-turn session (chat)

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

import FoundationModels
import FirebaseCore
import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Initialize a `geminiLanguageModel` with a Gemini model that supports your use case.
let model = ai.geminiLanguageModel(name: "gemini-3.5-flash")

// Create a session by injecting the model into Apple's `LanguageModelSession`.
// The session maintains state between each request.
let session = LanguageModelSession(model: model)

// Generate a text response to an initial prompt.
let response = try await session.respond(to: "Hello! I'd like to learn more about Albert Einstein.")
print(response.content)  // Example response from model: "What would you like to know?"

// Continue using the existing session. Each prompt and response is added to the transcript.
let response2 = try await session.respond(to: "When was he born?")
print(response2.content)  // Example response from model: "March 14, 1879"

Generate text from multimodal input (like images)

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

You can ask a Gemini model to generate text by prompting with text and a file, like an image or PDF.

import FoundationModels
import FirebaseCore
import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Initialize a `geminiLanguageModel` with a Gemini model that supports your use case.
let model = ai.geminiLanguageModel(name: "gemini-3.5-flash")

// Create a session by injecting the model into Apple's `LanguageModelSession`.
// For a single-turn interaction, create a new session each time you call the model.
let session = LanguageModelSession(model: model)

let cgImage: CGImage = // ... fetch CGImage from your datasource.
let response = try await session.respond {
  "What are the dominant colors of this image, in order?"
  Attachment(cgImage)
}
print(response.content)

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, use streamResponse instead of respond.

// imports
// initialization of Gemini API backend service and a `geminiLanguageModel`

// Create a session by injecting the model into Apple's `LanguageModelSession`.
// For a single-turn interaction, create a new session each time you call the model.
let session = LanguageModelSession(model: model)

let cgImage: CGImage = // ... fetch CGImage from your datasource.
let stream = session.streamResponse {
  "What are the dominant colors of this image, in order?"
  Attachment(cgImage)
}

var response = ""
for try await snapshot in stream {
  // The snapshot contains *all* content generated so far.
  response = snapshot.content
}
print(response)


Generate images (using "Nano Banana" models)

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

Models that support this capability

  • gemini-3-pro-image-preview (aka "Nano Banana Pro")
  • gemini-3.1-flash-image-preview (aka "Nano Banana 2")
  • gemini-2.5-flash-image (aka "Nano Banana")

You can ask a Gemini image-generating model (like a "Nano Banana" model) to generate an image by prompting with text-only input.

The following example shows how to generate only an image, but Gemini image-generating models can generate both images and text.

import FoundationModels
import FirebaseCore
import FirebaseAILogic

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Initialize a `geminiLanguageModel` with a Gemini image-generating model that supports your use case.
let model = ai.geminiLanguageModel(name: "gemini-2.5-flash-image"
    options:
      GeminiGenerationOptions(responseModalities: .image)
)

let session = LanguageModelSession(model: model)
let response = try await session.respond(
          to: "Generate an image of the Eiffel tower with fireworks in the background."
        )

var generatedImage: CIImage?
// Find the image in the transcriptEntries.
for entry in response.transcriptEntries {
  if case let .response(response) = entry {
    for segment in response.segments {
      if case let .attachment(attachment) = segment,
          case let .image(image) = attachment.content {
        generatedImage = image.ciImage
      }
    }
  }
}


Generate structured JSON output

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

Models that support this capability

  • gemini-3.1-pro-preview
  • gemini-3.5-flash
  • gemini-3.1-flash-lite

Gemini models return responses as unstructured text by default. However, some use cases require structured text, like JSON. For example, you might be using the response for other downstream tasks that require an established data schema.

You can configure the model to format its response according to a JSON schema that you supply. For details, best practices, and use cases for generating structured JSON output, see the general Generate structured output guide.

import FoundationModels
import FirebaseCore
import FirebaseAILogic

@Generable(description: "Basic profile information about a cat")
struct CatProfile {
  var name: String
  @Guide(description: "The age of the cat", .range(0 ... 20))
  var age: Int
  @Guide(description: "A one sentence profile about the cat's personality")
  var profile: String
}

// Initialize the Gemini Developer API backend service.
let ai = FirebaseAI.firebaseAI(backend: .googleAI())
// Initialize a `geminiLanguageModel` with a Gemini model that supports your use case.
let model = ai.geminiLanguageModel(name: "gemini-3.5-flash")
let session = LanguageModelSession(model: model)

let response = try await session.respond(
  to: "Generate a cute rescue cat profile with an Elvish theme",
  generating: CatProfile.self
)
let cat = response.content


Give feedback about accessing the Gemini API through Apple's Foundation Models framework