Benchmark Builder

How to use this benchmark builder

1. Write or paste two JavaScript snippets to compare.
2. Set how many iterations to average over.
3. Click "Run Benchmark" to see which one is faster in your browser.

Why do results vary between runs?

JavaScript engines use just-in-time compilation and various optimizations that can make timing slightly inconsistent between runs — averaging over more iterations gives a more reliable comparison.

JIT warm-up — why a snippet's first run is almost always its slowest

Modern JavaScript engines like V8 (used in Chrome and Node.js) don't execute your code the same way every single time — they use just-in-time (JIT) compilation, initially running code through a fast-starting but less optimized interpreter, then progressively compiling "hot" code (code that runs frequently) into significantly faster, more optimized machine code as the engine gathers more information about how that code is actually being used. This means a snippet's very first few iterations are almost always measurably slower than its later ones, purely because the engine hasn't finished warming up and optimizing it yet. This is exactly why averaging across a reasonable number of iterations — as this tool's iterations setting allows — produces a meaningfully more accurate result than a single run would: it lets the slower, unoptimized early iterations get diluted by the many faster, fully warmed-up ones that follow.

Dead code elimination — the silent trap that can invalidate a microbenchmark

A genuinely subtle pitfall in browser-based benchmarking is that JavaScript engines are specifically designed to detect and skip computing a result that's never actually used anywhere afterward, a standard optimization called dead code elimination. If a benchmarked snippet computes a value but that result is never read, logged, returned, or used in any way the engine can detect, a sufficiently aggressive optimizer may simply skip performing the computation altogether during a later, optimized pass — making the snippet appear implausibly, misleadingly fast simply because the engine correctly determined the actual computation was pointless busywork with no observable effect. This is exactly why properly written microbenchmarks conventionally use or return each computed result in some way, specifically to prevent the engine from being able to legally optimize the very computation being measured out of existence.

Garbage collection — an invisible pause that can quietly skew results

JavaScript automatically manages memory through periodic garbage collection, and a garbage collection pause can trigger at essentially any moment during a benchmark run, momentarily stopping JavaScript execution entirely to reclaim memory that's no longer being used. If a GC pause happens to land during one snippet's timed measurement but not the other's, purely by unlucky timing, it introduces a real, unrelated timing skew that has nothing to do with either snippet's actual computational efficiency. This is another concrete reason a single benchmark run should never be fully trusted on its own — running enough iterations to average out these essentially random GC-timing effects, exactly as this tool's iterations setting is designed to do, produces a genuinely more representative, more trustworthy comparison than any single isolated measurement possibly could.

Why a microbenchmark doesn't always predict real-world application performance

Even a carefully constructed microbenchmark measuring a small, isolated snippet in perfect isolation doesn't always translate cleanly and reliably into that same code's actual real-world performance once it's embedded inside a full, complex application. A snippet's performance in true isolation can differ meaningfully from its performance running alongside dozens or hundreds of other functions competing for the same JIT compiler's limited attention and optimization budget, the same shared memory allocator, and the same overall system resources. This doesn't make microbenchmarking pointless — it remains a genuinely useful way to compare two specific, competing approaches to the exact same well-defined task — but it's worth treating microbenchmark results as one meaningful input among several, rather than as the final, complete word on how a given piece of code will actually perform once deployed inside a real, full-scale application.

Limitations of this tool

This tool runs both snippets directly in your own browser tab and averages their execution time across your chosen number of iterations — as covered above, it's a genuinely useful rough comparison for quick, everyday decisions, but for rigorous, publication-grade benchmarking, dedicated tools that specifically account for JIT warm-up phases, isolate against dead code elimination, and control for garbage collection timing produce more scientifically reliable results. Results will also vary somewhat across different browsers, different devices, and different JavaScript engine versions, since each has its own specific optimization behavior — a result measured here reflects your specific browser and device at this specific moment, not a universal, engine-independent truth about which approach is definitively faster everywhere.