COMPARISON · MEASURED 2026-09
Image Compression Tool Comparisons
Head-to-head comparisons between ImgFlow and TinyPNG, Squoosh, iLoveIMG, and more. Honest, verified benchmarks on offline privacy, format support, visual quality, and file caps.
ImgFlow vs TinyPNG: free limits, file-size caps, and local processing
TinyPNG free tier caps you at 5 MB per image, 20 compressions and 3 conversions per session, with programmatic ads. ImgFlow has no per-image cap, no session quota, and strips EXIF/GPS by default — also processing locally in the browser. Verified September 2026.
Where ImgFlow wins
- No 5 MB per-image cap, no 20-per-session quota — the free tier is the full product
- A target-size engine that hits exact sizes (100 KB / 500 KB) across 21 scenario-tuned tool pages
- EXIF/GPS stripped by default; no programmatic ad auction or tracking pixels on the free tier
ImgFlow vs Squoosh: fine-tune one image, or ship a hundred
Squoosh and ImgFlow both process 100% locally in the browser (our test saw zero third-party requests). Squoosh is Google’s open-source single-image tuner, famous for side-by-side comparison and experimental JXL/WP2/QOI codecs; ImgFlow focuses on shipping batches: unlimited queue + ZIP, exact target sizes, platform presets. Verified September 2026.
Where ImgFlow wins
- A complete batch shape: hundreds queued, failures never block, one-click ZIP — no per-file babysitting
- Target-size engine + grouped scene presets: hitting 100 KB or social/listing/avatar specs is one click
- A dedicated page per format × scenario in 5 languages — searching “HEIC to JPG under 100 KB” lands you exactly there
ImgFlow vs iLoveIMG: should your images leave your device?
iLoveIMG is a broad online image suite (compress/crop/watermark/AI tools + a PDF ecosystem) built on the cloud model: our September 2026 test caught an upload to api33.iloveimg.com the moment we dropped an image. ImgFlow compresses and converts in the browser: 0 upload endpoints, no per-task quota, works offline. Here is the trade-off, with measured data.
Where ImgFlow wins
- 0 upload endpoints: images never leaving the device is architectural, and provable by going offline
- Core tools free forever: no task quota, core features need no registration
- EXIF/GPS stripped by default — in the upload model metadata rides the original file to the server first; in the local model it never leaves home