Quick Sort vs Tim Sort

Quick Sort is often fastest on uniformly random arrays, but it is unstable and has an O(n²) worst case. Tim Sort is stable, guarantees O(n log n), and reaches O(n) on partially-sorted data by detecting runs. Since real inputs are frequently partially ordered, Tim Sort often matches or beats Quick Sort on practical workloads.

A general-purpose library sort must behave predictably for everyone. Stability enables correct multi-key sorting, which countless applications depend on. Quick Sort cannot offer stability without giving up its in-place advantage, so language designers chose Tim Sort (for objects) and reserved Quick Sort variants for primitives where stability does not matter.

For a custom hot loop on random primitive data, Quick Sort or Introsort may be faster. For a default that must be stable and robust on real data, Tim Sort wins — which is why it is everywhere. See what algorithm Python uses for the full story.