JPG vs WebP vs AVIF: Which Image Format Should You Use?

JPG, WebP, and AVIF are all common ways to store photographs for the web. None of them is universally “best.” The useful choice depends on compatibility, transparency needs, and how small you need the file to be.

What is JPG?

JPG (JPEG) is the long-standing format for photographs. It uses lossy compression: some detail is discarded to reduce file size. It does not support transparency. Almost every camera, phone, and browser can open JPG files.

What is WebP?

WebP is a Google-designed format used widely on websites. It can store lossy photos and lossless graphics, and it supports transparency. Many sites convert JPG or PNG to WebP to reduce page weight while keeping a familiar workflow.

Convert JPG to WebPRuns in your browser—files are not uploaded for conversion.

What is AVIF?

AVIF is a newer format based on AV1 video technology. It often achieves strong compression for photographs and can include transparency. Support in modern browsers has improved, but older devices and some apps still prefer JPG or WebP.

Convert JPG to AVIF

Feature comparison

Compression differences

At similar visual quality, WebP and AVIF often produce smaller files than JPG for the same photo. The savings are not fixed. Busy textures, noise, and sharp edges behave differently. Always judge the result with your eyes and a real file-size check.

If you need a hard size limit, combine format choice with quality or target-size compression.

Transparency

JPG cannot keep a transparent background. WebP and AVIF can. For logos and UI graphics with alpha, start from PNG (or another alpha source) and convert carefully—flattening to JPG will fill transparent areas.

Which format should you choose?

• Need maximum compatibility or camera originals → keep or export JPG.
• Need smaller web photos with broad modern support → try WebP.
• Need aggressive size reduction for current browsers → try AVIF and verify display.
• Need transparency → prefer WebP or AVIF (or PNG), not JPG.

A practical approach is to convert a sample, compare quality at a given size, then batch the rest. Tools on this site run locally so you can experiment without uploading.