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@@ -26,10 +26,11 @@ All variants take and return fp32 tensors — swap the `.pte` file, keep your ap
 
 
 The Core ML build is the same graph lowered to Apple's Neural Engine instead of
-XNNPACK, which is CPU-only. On an iPhone 17 Pro, Depth-Anything-V2-Small runs
-500.8 ms through XNNPACK and 42.7 ms through Core ML, at half the file size. It
-computes in fp16 and is iOS-only; the XNNPACK files stay the portable option and
-are what runs on Android.
+XNNPACK, which is CPU-only. Measured on an iPhone 17 Pro across seven models, it
+runs **3.5x to 13.9x faster (median 12x)** at roughly half the file size — for
+example Depth-Anything-V2-Small at 500.8 ms against 42.7 ms, and MODNet at 81.7 ms
+against 5.9 ms. It computes in fp16 and is iOS-only; the XNNPACK files stay the
+portable option and are what runs on Android.
 
 \*Mac arm64, single process, median of 10 — a reference point for relative cost
 only, not a device number (torch eager fp32 on the same machine: 38.6 ms).