Overview

QR code generation is a batch workload in many systems: product labels, event ticketing, URL shorteners, and payment systems all generate large volumes on demand. The encoding algorithm is fixed by the ISO 18004 standard — both qrcode (Python) and QRCoder (.NET) implement identical Reed-Solomon error correction and matrix placement.

Since the algorithm is standardized, this benchmark directly measures how much of the runtime is language overhead versus actual QR encoding work.

Benchmark Setup

50,000 QR codes generated from random 12-character alphanumeric strings:

• Error correction level M (15% recovery capacity)
• Version auto-selected (typically version 3–4 for 12-char payloads)
• Output: dark-module count and matrix size (no image rendering — pure encoding)

Tested at 1,000 / 10,000 / 50,000 codes. Python uses qrcode.QRCode(error_correction=ERROR_CORRECT_M, border=0). .NET uses QRCoder.QRCodeGenerator with ECCLevel.M.

Results

Why QRCoder Is Faster

QR encoding involves three expensive steps:

1. Data encoding — convert input bytes to QR codewords using mode selection and character set tables
2. Reed-Solomon error correction — polynomial multiplication in GF(256), repeated for every block
3. Matrix placement — fill the QR matrix with function patterns, data bits, and masking

In Python, each step iterates over lists of integers with per-iteration interpreter overhead. The GF(256) multiplication table lookup is a Python dict access. In .NET, all three steps compile to tight loops over int[] arrays with no boxing, no dict hashing, and no object allocation per symbol.

The border=0 / QuietZone=4 setting also matters: Python's default rendering calculates quiet zone positions in Python; the .NET implementation handles this in a single matrix-copy operation.

Key Code

// QRCoder — generate and count dark modules
// Replaces: qrcode.QRCode(error_correction=ERROR_CORRECT_M, border=0)
public Result Analyze(string text)
{
    using var qrGenerator = new QRCodeGenerator();
    using var data = qrGenerator.CreateQrCode(text, QRCodeGenerator.ECCLevel.M);
    var matrix = data.ModuleMatrix;
    long dark = 0;
    for (int r = 0; r < matrix.Count; r++)
        for (int c = 0; c < matrix[r].Count; c++)
            if (matrix[r][c]) dark++;
    return new Result(dark, matrix.Count);
}

# qrcode — pure Python Reed-Solomon + matrix placement
qr = qrcode.QRCode(error_correction=qrcode.constants.ERROR_CORRECT_M, border=0)
for text in batch:
    qr.clear()
    qr.add_data(text)
    qr.make(fit=True)
    matrix = qr.get_matrix()
    dark   = sum(1 for row in matrix for cell in row if cell)

Both produce identical matrices. The .NET version's Reed-Solomon polynomial arithmetic runs as compiled native code rather than Python bytecode.

Diagrams

At 1,000 codes Python is ~6× slower. At 50,000 codes it's nearly 14×. The growing gap shows that per-code overhead (GF(256) table lookups, list operations) accumulates faster than any fixed startup cost.

For a ticketing platform generating 100,000 QR codes on demand: Python takes ~59 seconds, .NET takes ~4 seconds. The difference determines whether this can be a synchronous API call or requires a queue.