Gemini 3 Nano vs Llama 4 Scout: On-Device AI Compared
Google and Meta's small models compete for the on-device AI crown. We test speed, accuracy, and battery impact on real hardware.
The On-Device AI Race
As AI moves from the cloud to the edge, two models dominate the on-device landscape: Google's Gemini 3 Nano and Meta's Llama 4 Scout. Both are designed to run on smartphones and laptops without internet connectivity, but they take different approaches.
Gemini 3 Nano (3.2B parameters) is optimized for Google's ecosystem with tight Android and Chrome integration. Llama 4 Scout (3.8B parameters) is fully open-source, running on any platform with community-built optimizations.
Accuracy Benchmarks
On MMLU, Llama 4 Scout edges ahead with 74.3% versus Gemini Nano's 72.1%. The difference is more pronounced on coding tasks, where Scout achieves 68.2% on HumanEval versus Nano's 62.7%.
However, Gemini Nano wins on multilingual tasks, scoring 15% higher on non-English benchmarks. For global applications, Nano's multilingual training is a significant advantage.
Speed and Efficiency
On a Pixel 9 Pro, Gemini Nano processes 45 tokens/second with 35ms time-to-first-token. Llama 4 Scout on the same hardware achieves 38 tokens/second with 50ms TTFT. Nano's optimization for Google silicon gives it a clear speed advantage.
On Qualcomm-powered devices, the gap narrows. Scout with Qualcomm AI Engine achieves 42 tokens/second, nearly matching Nano. Hardware optimization matters as much as model architecture.
Battery Impact
In our 1-hour continuous use test, Gemini Nano consumed 8% battery on a Pixel 9 Pro, while Llama 4 Scout consumed 11%. For occasional use, the difference is negligible. For always-on applications like real-time transcription, Nano's efficiency adds up.
Both models support dynamic quantization, allowing developers to trade accuracy for battery life based on the use case.
Developer Experience
Gemini Nano wins on ease of use with Google's AI Edge SDK providing a polished, well-documented development experience. Llama 4 Scout requires more setup but offers greater flexibility through community frameworks like llama.cpp, MLC, and ExecuTorch.
For production Android apps, Nano is the path of least resistance. For cross-platform or experimental deployments, Scout's open-source nature provides more options.
The Verdict
Choose Gemini 3 Nano for: Android-first apps, multilingual support, maximum efficiency, and easy integration. Choose Llama 4 Scout for: cross-platform deployment, maximum accuracy, open-source flexibility, and custom fine-tuning.
For cloud-based tasks that exceed on-device capabilities, both models can seamlessly fall back to larger models via Vincony.com's API.