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Free Big O calculator for students and engineers. Compare asymptotic growth, estimate rough runtime, and evaluate algorithm scalability with our programming calculator suite.
Last updated: April 14, 2026
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Use your rough machine throughput for runtime estimation.
Notation
O(n log n)
Growth Category
Good
Estimated Operations
1,660,964
Estimated Runtime
16.61 ms
Growth Profile
Linearithmic growth. Preferred for general-purpose sorting and divide-and-conquer.
Runtime estimate assumes one dominant operation and ignores constants/lower-order terms.
Supported classes
O(1) to O(n!)
Covers core growth classes used in interviews and production engineering.
Scale awareness
Input-sensitive
Shows how rapidly operation counts explode for poor complexity classes.
Time estimate
Human-readable
Converts operation totals into ms/sec/min/hour/day/year scale.
CS fundamentals
Practice-ready
Great for visualizing why algorithmic choices matter in coding interviews.
Performance guardrail
Growth-first
Quickly spot classes that become impractical at production scale.
Learning value
Contextual
Pairs each class with practical guidance for real-world usage.
For O(n log n), n = 100,000, and 100M ops/sec:
Estimated Operations
1,660,964
Estimated Runtime
~16.6 ms
The calculator models dominant-term growth for each complexity class, estimates operation counts from input size n, and then converts operations into a rough runtime estimate based on your throughput setting.
operations = f(n) based on selected Big O classruntime = operations / operations-per-secondfocus = dominant term growth behaviorBig O highlights scalability trends, not precise wall-clock execution for every environment.
Visualizes why O(n^2), O(2^n), and O(n!) degrade quickly
Asymptotic analysis comes from algorithm theory and is fundamental in computer science. It abstracts machine specifics so engineers can compare algorithm scalability in a platform-independent way before optimization.
Explore more programming tools like our modulo calculator and regex match calculator.
Get Custom Developer Tool for Your PlatformResult: Better asymptotic complexity is often the biggest optimization available.
Switching from O(n^2) to O(n log n) or O(n) can outperform low-level micro-optimizations.
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