On-device LLMs in 2026: Gemini Nano vs Apple Intelligence

Two years ago, running an LLM on-device was a science project. In 2026 it is a deploy target. Both Google and Apple ship first-party on-device models with public APIs, the RAM ceiling finally fits 2-4B parameter models, and power draw is defensible for features you run a few times a minute.

What we ship on-device today

• Smart reply drafting in chat apps · 80-150ms, no spinner.
• Receipt / invoice field extraction · fully offline, GDPR-trivial.
• Photo caption + search index on-device.
• Meeting transcription + bullet summary (with Whisper.cpp + a local summariser).

Gemini Nano · where it wins

• Available on a much wider device matrix · Android 15+ with 8GB+ RAM.
• AICore handles model updates without a playstore ship.
• Summarisation + rewriting APIs are stable and predictable.
• Works better with languages beyond English than Apple Intelligence on mid-range hardware.

Apple Intelligence · where it wins

• A17 Pro / M-series only · narrower matrix, but the models are meaningfully better.
• The Writing Tools API is a drop-in replacement for a cloud call · zero glue code.
• Private Cloud Compute fallback is automatic and audit-friendly.
• Foundation-models API surface is more coherent · one SDK, not three.

Hungarian-language reality check

Both models underperform on Hungarian vs English. In our evals Gemini Nano gives ~85% acceptable-output rate on Hungarian summarisation, Apple Intelligence ~80%. For comparison, Claude 3.7 Haiku is ~97%. For Hungarian-heavy features we keep a cloud fallback for now.

When we still call the cloud

1. Any agentic flow with tool calls · on-device tool-use is fragile.
2. Long-context tasks (> 8k tokens effective) · on-device context windows are still small.
3. Safety-critical outputs · medical, legal, financial advice · we route to a policy-gated cloud call with audit logs.
4. Multilingual features where non-English quality matters for conversion.