Choose FastVLM when
- Inputs are dense documents or UI screenshots at 1024 px and above
- You deploy on iPhone or Mac with MLX or Core ML
- You want the reference 0.5B browser demo Apple links from its research page
FASTVLM VS SMOLVLM2
FastVLM vs SmolVLM2
Both families target devices, not data centres. FastVLM optimises time-to-first-token on high-resolution images with its FastViTHD encoder; SmolVLM2 pushes size down to 256M and adds video. This page compares what is documented, not what a single benchmark table implies.
All comparisons
| Feature | FastVLM | SmolVLM2 |
|---|---|---|
| Developer | Apple researchers (CVPR 2025) | Hugging Face (HuggingFaceTB) |
| Sizes | 0.5B · 1.5B · 7B | 256M · 500M · 2.2B |
| Vision encoder | FastViTHD hybrid encoder; fewer visual tokens at high resolution | SigLIP-family encoder with pixel-shuffle token compression |
| Image input | Yes | Yes |
| Video input | Not in the official models (apps process frames one at a time) | Yes — Video-Instruct variants at 256M, 500M and 2.2B |
| Multi-image prompts | Single image per prompt in official examples | Yes |
| Browser (WebGPU) | 0.5B via onnx-community/FastVLM-0.5B-ONNX (this site’s playground) | SmolVLM 256M/500M have Transformers.js demos; check the SmolVLM2 card for ONNX |
| MLX / Apple Silicon | Official MLX app for iOS and macOS; mlx-vlm | MLX from day zero (Python and Swift) |
| License | Apple ML Research license (read the terms) | Apache 2.0 |
| Headline claim | Up to 85× faster TTFT than LLaVA-OneVision-0.5B at 1152 px (Apple) | Smallest video-capable VLMs; 500M close to 2.2B on video (Hugging Face) |
Checked 2026-09-17
Decision summary
- Pick FastVLM for high-resolution screenshots, receipts and documents where first-token latency matters, especially on Apple hardware.
- Pick SmolVLM2 when you need video, multi-image prompts, a sub-500M model or an Apache 2.0 license.
- Both run in the browser at their smallest sizes; FastVLM-0.5B is the one you can test on this site right now.
Choose SmolVLM2 when
- You need short-video understanding or several images per prompt
- Memory is extremely tight (256M–500M)
- License simplicity matters for a commercial product
How to read this table
Published scores for the two families come from different papers, prompts and evaluation code. Do not rank them from a merged table; run both on your own images with the same resolution and device.
Questions
Is SmolVLM2 more accurate than FastVLM?
There is no apples-to-apples published comparison. FastVLM-0.5B reports DocVQA 82.5 and TextVQA 64.5 in Apple’s table; SmolVLM2’s cards report their own suites. Test on your data.
Which is faster in the browser?
It depends on the GPU, quantization and image size. Use the device benchmark on this site to measure FastVLM-0.5B and compare against a SmolVLM demo on the same machine.