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You can use Gemini Nano in an Android app built with Capacitor by calling Google’s ML Kit GenAI Prompt API from native Android code and exposing the operations through a Capacitor plugin. Google documents the Android/Kotlin and Java API, not a Capacitor-specific JavaScript package, so the bridge is part of your app’s integration. You must also check at runtime whether the device can use the model and provide a useful path when it cannot.
How Gemini Nano fits into a Capacitor app
Gemini Nano runs on-device through Android AICore. The documented app-facing route is ML Kit’s GenAI API family, including the Prompt API. It is not an official browser API or a JavaScript package you can call directly from a Capacitor web view. See Android Developers’ Gemini Nano overview and the ML Kit Prompt API setup guide.
In a Capacitor project, the practical architecture is a native Android plugin that wraps the ML Kit calls and presents a typed JavaScript interface to your web layer. That bridge is an integration design choice based on Google’s native documentation; Google does not publish a Capacitor-specific implementation.
What the Prompt API can do
The Prompt API accepts text or image-plus-text input and can return a complete result or stream output incrementally. A Capacitor plugin can expose only the operations your app needs, such as readiness checks and text generation, or also expose multimodal input and streaming when the interface requires them. Keep cancellation, errors, and status changes explicit in the JavaScript contract rather than hiding them inside a single opaque call. See Google’s Prompt API feature documentation.
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Set up the native Android dependency
Google’s setup guide lists the dependency implementation("com.google.mlkit:genai-prompt:1.0.0-beta4") and requires Android API level 26 or later. The version shown is beta-labeled and may change, so check the live setup page before adding it to a new project. Meeting the Android API minimum does not mean a device has a compatible model or configuration.
Implement the ML Kit calls in the Android side of your Capacitor plugin, then define typed JavaScript methods for the UI. Keep the native boundary narrow: for example, expose a status operation, a generation operation, and—if needed—a streaming operation. Map native failures and status transitions into documented plugin results so the web layer can respond appropriately.
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Check model availability before generating
Initialize the ML Kit GenerativeModel and call checkStatus() before offering inference. Google documents four states that your app should distinguish:
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- DOWNLOADABLE: The model can be downloaded. Offer preparation and explain that the feature is not ready yet.
- DOWNLOADING: Show progress or a clear waiting state, and handle a failed or interrupted download.
- AVAILABLE: The model is ready for requests.
Do not block the interface while the model is being prepared. Refresh or observe readiness as appropriate for your flow, and make the unavailable and download-failure paths part of the plugin contract. The state names and behavior are in Google’s setup guide.
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Device support is not determined by Android version alone
Android API 26 is the Prompt API dependency minimum, not a guarantee of Gemini Nano availability. AICore and supported model configurations vary by device. Google’s ML Kit GenAI device overview, last updated 2026-09-28, lists support by API and Nano version; its Prompt API list includes Google Pixel 10 for nano-v3, as well as separately listed supported models for other Nano versions. Check the current matrix and the result of checkStatus() for your target configuration.
Do not infer Prompt API support from a device’s support for another GenAI feature. The overview’s device lists for summarization, proofreading, rewriting, and image description differ from its Prompt API list.
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Keep prompts and outputs bounded
Google documents a prompt input limit under 4,000 tokens—approximately 3,000 English words—and advises avoiding outputs over 4K tokens. Keep tasks focused, and handle requests that exceed your chosen bounds. A shorter prompt does not remove per-app inference quotas or guarantee a response.
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Expect foreground and quota constraints
Inference is permitted only while your app is the top foreground application, according to the ML Kit GenAI overview. Do not design a background job around this API. Handle quota or busy conditions gracefully, for example by letting the user retry later or continue with a non-AI workflow.
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Choose model configurations deliberately
Google documents multiple Nano versions and configuration-specific device support. Its model selection guide, last updated 2026-04-13, recommends falling back from unavailable preview configurations and using Stable for public production releases. The same prompt can produce different results across model versions, so avoid relying on identical output across devices.
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When the model is unavailable, downloading, quota-limited, or inaccessible because the app is not in the foreground, the user still needs a clear next step. Provide a non-AI alternative where possible. If you choose to add a remote AI fallback, make it a separate, disclosed product decision: the local API does not automatically switch to cloud inference. The prompts and inference handled by AICore are local, but your own app’s logging, collection, synchronization, or separately implemented remote fallback remain your responsibility.
Build and verify the Capacitor bridge
- Add the native ML Kit integration: Use the current dependency and Android minimum from the Prompt API setup guide, checking the live page because the documented version can change.
- Expose status first: Add a plugin method that initializes the model and returns the relevant
checkStatus()state to JavaScript. - Represent preparation: If the state is downloadable or downloading, give the UI a way to show progress and handle failures without freezing the app.
- Add inference methods: Expose the text or image-plus-text inputs your feature needs, and streaming only if the interface uses incremental output.
- Define failures and lifecycle behavior: Make unavailable models, quota limits, cancellation, and foreground restrictions visible to the web layer, then give users a fallback or retry path.
- Test on supported configurations: Verify runtime status on actual target devices against Google’s current device matrix; do not use Android version or a device’s unrelated Gemini feature as a proxy.
Choosing a wrapper or writing your own plugin
A third-party Capacitor wrapper may reduce setup work, while a custom plugin gives you direct control over status, downloads, generation modes, streaming, and errors. Before adopting any wrapper, verify that its current release supports your Capacitor and Android versions and exposes the specific API operations and states your app needs. Treat marketing claims about device compatibility as insufficient: compare the wrapper’s documented behavior with Google’s current API and device matrix, then test checkStatus() on your target devices.
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