Choose FastVLM when
- Image-only input at high resolution
- Browser demo without a vendor runtime
- Apple-first deployment
FASTVLM VS GEMMA 4 / GEMMA 3N
Gemma 4 (April 2026) and Gemma 3n (2025) are Google’s general multimodal models for phones and laptops; FastVLM is a vision-first model. The difference in scope matters more than any score: Gemma answers with audio, long context and agent tooling, FastVLM concentrates on fast, high-resolution image understanding.
| Feature | FastVLM | Gemma 4 (E2B / E4B) | Gemma 3n (E2B / E4B) |
|---|---|---|---|
| Developer · release | Apple · 2025 | Google · 2 April 2026 | Google · 2025 |
| On-device sizes | 0.5B · 1.5B · 7B | E2B · E4B (larger 12B, 26B-A4B, 31B for workstations) | E2B · E4B (effective parameters) |
| Modalities | Image + text | Image, video as frames, text; audio on E2B/E4B | Image, audio, text |
| Context window | Short prompts; not a long-context model | 128K tokens (E2B/E4B, Google) | 32K tokens (Google) |
| Browser | 0.5B via Transformers.js (this site) | Runtime-dependent (LiteRT / MediaPipe style deployments) | Runtime-dependent (Google AI Edge) |
| MLX / Apple Silicon | Official MLX app | Community mlx-vlm conversions | Community mlx-vlm conversions |
| License | Apple ML Research license | Gemma Terms of Use | Gemma Terms of Use |
Checked 2026-09-17
Gemma sizes are “effective” parameter counts (E2B ≈ 2.3B dense-equivalent memory) and their multimodal benchmarks use Google’s harness; FastVLM’s numbers come from Apple’s. The table records documented capabilities, not measured quality.
Check the current Transformers.js model list; Google’s reference path for browsers and phones is its own AI Edge runtime. FastVLM-0.5B already has an ONNX build you can try here.