Quick Sort vs Merge Sort
'Quick Sort vs Merge Sort' is one of the most searched and most-debated topics in computer science — and for good reason. Both are elegant O(n log n) divide-and-conquer algorithms, yet they make opposite trade-offs. This guide compares them across every dimension that matters so you can pick the right one.
Head-to-head comparison
| Property | Quick Sort | Merge Sort |
|---|---|---|
| Average time | O(n log n) | O(n log n) |
| Worst time | O(n²) | O(n log n) |
| Space | O(log n) | O(n) |
| Stable | No | Yes |
| In-place | Yes | No |
| Cache locality | Excellent | Poorer |
Why Quick Sort is usually faster
Despite identical average complexity, Quick Sort typically runs 2-3x faster on in-memory arrays. It sorts in place with sequential memory access, so data stays in CPU cache, and its inner loop is tighter. Merge Sort's O(n) buffer causes cache misses and allocation overhead. Read the deep dive in why Quick Sort is faster.
Where Merge Sort wins
Merge Sort wins when you need stability, a guaranteed O(n log n) worst case, or are sorting linked lists (which it handles with O(1) extra space). It is also the basis of external sorting for data too big for RAM, because it streams data sequentially. That reliability is why Java uses it for objects.
Which should you choose?
For general in-memory array sorting, Quick Sort (or its hybrid Introsort) is the practical winner. When stability or worst-case guarantees matter, choose Merge Sort (or its hybrid Tim Sort). Most standard libraries actually use these hybrids. Watch both run side by side in the visualizer.
Frequently asked questions
Is Quick Sort or Merge Sort faster? +
Why use Merge Sort if Quick Sort is faster? +
Which is better for large data? +
See it in motion
Watch this algorithm and 9 others run step by step in our free interactive visualizer.
▶ Launch Visualiser