---
title: "On-device AI in mobile apps: Gemini Nano vs Apple's Foundation Models"
description: "Android and iOS both ship a language model on the phone now. What Gemini Nano and Apple's Foundation Models can do, where they fall short, and how to start."
author: "MH TOUFIK"
published: 2026-10-30T18:23:37.435Z
updated: 2026-10-30T19:23:37.000Z
url: https://www.toufikforyou.dev/blog/on-device-ai-gemini-nano-vs-apple-foundation-models
category: "Mobile AI"
tags: ["Android", "Foundation Models", "Gemini Nano", "ML Kit", "On-device AI", "Swift"]
---

# On-device AI in mobile apps: Gemini Nano vs Apple's Foundation Models

A couple of years ago, adding AI to a mobile app meant one thing: send the user's text to a server, wait, and pay for every request.

That's no longer the only option. Both Android and iOS now ship a language model **on the phone itself**, and both give developers an API to use it. No server, no per-request bill, and the user's data never leaves the device.

I've been reading through both platforms' documentation to understand what's actually possible. Here's the short version.

## Why on-device matters

Running a model on the device changes four things at once:

- **Privacy.** The text stays on the phone. For notes, messages, health data, or anything personal, that's a big deal.
- **Offline.** It works on a plane, in a basement, or on a bad connection.
- **Cost.** There's no API bill that grows with every user.
- **Speed.** No network round-trip, so short tasks feel instant.

The trade-off: these are small models. They're good at specific jobs, not at being a know-it-all chatbot.

![How on-device AI is wired on Android and iOS](https://www.toufikforyou.dev/media/on-device-ai-stack-6d2f90c1.webp "Android routes requests through ML Kit and the AICore system service to Gemini Nano. iOS uses the Foundation Models framework.")

## Android: Gemini Nano through ML Kit

On Android, the on-device model is **Gemini Nano**. Apps don't load it themselves. It runs inside **AICore**, a system service, and developers reach it through the **ML Kit GenAI APIs**.

According to Google's documentation, the ready-made APIs cover:

- **Summarization** — turn a long article or chat thread into a few bullet points
- **Proofreading** — fix grammar and spelling
- **Rewriting** — change the tone or style of a message
- **Image description** — generate a short description of an image

There's also a lower-level **Prompt API** if you need something the task APIs don't cover.

The catch: Gemini Nano only runs on supported devices. Your app must check whether the feature is available — and possibly downloadable — before showing it to the user.

## iOS: Apple's Foundation Models framework

On iOS, Apple introduced the **Foundation Models framework** at WWDC 2025. It gives Swift code direct access to the on-device model behind Apple Intelligence — according to Apple's machine learning research team, a model of roughly 3 billion parameters.

The feature I like most is **guided generation**. Instead of asking for text and then parsing it, you describe a Swift type and the model fills it in:

```swift
import FoundationModels

@Generable
struct ReadingNote {
    @Guide(description: "A one-sentence summary of the passage")
    var summary: String

    @Guide(description: "Up to three key ideas, each a short phrase")
    var keyIdeas: [String]
}

let session = LanguageModelSession()
let response = try await session.respond(
    to: "Summarize this passage: \(passage)",
    generating: ReadingNote.self
)

print(response.content.summary)
```

Apple explains that the system uses constrained decoding, so the output is guaranteed to match your type. No more broken JSON.

Apple is also clear about the limits: the on-device model is tuned for tasks like **summarization, extraction, and classification**. It isn't meant for world knowledge or advanced reasoning.

## So which tasks actually fit?

Both platforms point to the same sweet spot. On-device models are great at **working with text the user already has**:

- Summarizing a long note, email, or chapter
- Pulling out structured data — dates, names, to-do items
- Tagging or categorizing content
- Suggesting a shorter or friendlier rewrite

They're a poor fit for answering factual questions ("Who won the 2018 World Cup?"), where small models can confidently make things up.

When I think about my reading app, [Books My Friend](https://github.com/toufikforyou/books-my-friend-iOS), the obvious use is exactly this kind of task: summarising a chapter or pulling out its key ideas — the user's own text, processed privately on their own phone.

## Five rules if you're adding it to your app

1. **Feature-detect first.** Not every device supports these models. Check availability and hide the feature cleanly when it's missing.
2. **Always have a fallback.** Either a simpler non-AI path or a clear message. Never a broken button.
3. **Keep prompts small.** On-device models have limited context. Send the relevant chunk, not the whole document.
4. **Use structured output.** On iOS, `@Generable` types. On Android, ask for a specific format and validate it.
5. **Be honest in the UI.** Label AI-generated text, and let users edit it. A summary is a suggestion, not the truth.

## The bottom line

On-device AI isn't a gimmick anymore. On both platforms it's a supported, documented API that's good at a specific set of jobs: summarize, extract, classify, rewrite.

If your app handles user text, there's probably one feature where an on-device model saves your users real time — privately, offline, and without a server bill. Start with that one feature.

## Sources

- [Gemini Nano and AICore](https://developer.android.com/ai/aicore) — Android Developers
- [Build intelligent experiences on Android with AI](https://developer.android.com/ml) — Android Developers
- [Foundation Models framework](https://developer.apple.com/foundation-models-framework/) — Apple Developer
- [Meet the Foundation Models framework (WWDC25)](https://developer.apple.com/videos/play/wwdc2025/286/) — Apple Developer
- [Updates to Apple's On-Device and Server Foundation Language Models](https://machinelearning.apple.com/research/apple-foundation-models-2025-updates) — Apple Machine Learning Research