As JPEG XL gains support across major web browsers, its performance relative to AVIF—another popular modern image format—has become a key consideration for web developers. A comparative analysis of these two codecs shows that for the most common web use-case—lossy photos—AVIF generally produces smaller files. At default effort settings, the best JPEG XL versions are typically 15–25% larger than equivalent AVIF files. Even at maximum effort settings, the gap remains significant, usually ranging between 6% and 20%. While JPEG XL can occasionally outperform AVIF on specific images, AVIF remains the more efficient choice for standard photographic content.
Beyond photography, the performance gap widens for specific types of digital content like screenshots and illustrations. In these cases, AVIF is significantly more efficient, with JPEG XL files often being 30–90% larger at default settings. This disparity is even more pronounced for images containing transparency; JPEG XL's `libjxl` encoder produces files that are 31–44% larger than AVIF at default settings. Furthermore, while the `jixel` encoder offers faster processing and lower memory usage than AVIF, its resulting files are typically much larger, making AVIF the better balance for most production environments.
Technical trade-offs also exist regarding decoding speed and specialized features. JPEG XL typically takes 2–3x longer to decode than AVIF at medium quality levels, though this gap narrows at very high qualities. For high-dynamic-range (HDR) content, JPEG XL faces a notable limitation: while the format supports gain maps, current browser decoders do not, making it less effective for HDR than AVIF. However, JPEG XL remains the clear winner for lossless compression, where it produces files significantly smaller than both WebP and AVIF. For most standard web needs, the analysis suggests defaulting to AVIF 4:4:4, while considering JPEG XL via the `jixel` encoder only if AVIF produces unexpectedly large files for a specific image.
One significant caveat involves the maturity of the encoders. The `libjxl` encoder's maximum effort settings are notably slow—sometimes taking up to 180x longer than default settings—due to iterative optimization loops. While newer encoders like `jixel` improve these speeds, the overall ecosystem is still evolving. Because different encoders and effort levels can produce wildly different results, developers are encouraged to test specific images rather than relying on general benchmarks, as even small changes in codec versions can shift the performance balance between these two formats.