Staaarter Tools

Big Integer Generator

Generates a requested count of random BigInt values with exactly a requested number of digits (the first digit is always non-zero), one per line, for testing code that handles arbitrarily large integers. A free online tool from Staaarter, right in your browser.

Runs locallyUpdated 2026-07-27
integergeneratorrandombigint

Overview

Introduction

This tool generates random integers far larger than JavaScript's built-in Number type can represent precisely, using BigInt arithmetic throughout.

Choose a digit length from 5 to 100 and a count, and it prints that many random values of exactly that length, one per line.

What Is Big Integer Generator?

A generator that produces random BigInt values with an exact requested digit count, the first digit always drawn from 1-9 and the rest from 0-9.

It's built for stress-testing code paths meant to handle arbitrarily large integers, well beyond the ~16-digit precision limit of a regular JavaScript Number.

How Big Integer Generator Works

For each value, the tool first picks a non-zero digit (1-9) for the leading position, then picks Digit Length - 1 further random digits (0-9) for the rest.

The resulting digit string is converted through BigInt to normalize it (via BigInt(digits).toString(10)) and printed.

When To Use Big Integer Generator

Use it to generate test data for code that parses or computes with arbitrarily large integers (e.g. cryptographic key material sizing, big-number libraries).

It's also useful for stress-testing input validation that claims to support 'any size' integer.

Often used alongside Small Integer Generator and Integer Information Printer.

Features

Advantages

  • Produces values with an exact, guaranteed digit count (no accidental leading zeros shrinking the effective length).
  • Uses BigInt throughout, so there's no precision loss even at 100 digits.
  • Supports batch generation (multiple values per click) for quick test-data creation.

Limitations

  • Digit length is capped at 100 to keep generation and rendering fast in the browser.
  • Uses Math.random() per digit, so it is not suitable for cryptographic or security-sensitive randomness.
  • Does not guarantee primality, uniqueness, or any other number-theoretic property beyond digit count.

Examples

One 5-digit value

Input

count=1, digitLength=5

Output

48213

A single random 5-digit integer with a non-zero leading digit (exact value varies run to run).

Two 20-digit values

Input

count=2, digitLength=20

Output

73920481950284710293
10938475620198374652

Two independent 20-digit BigInt values, each with a non-zero leading digit.

Best Practices & Notes

Best Practices

  • Use a modest digit length (under 20) if you plan to read the values by eye.
  • Remember these are strings/BigInts in most languages; converting to a regular floating-point number will lose precision above ~15-17 digits.

Developer Notes

Digits are built as a string (`String(1 + Math.floor(rng() * 9))` for the leading digit, then `String(Math.floor(rng() * 10))` per remaining digit) and only converted with `BigInt(digits)` (not a `123n` literal, per this codebase's ES2017 target) to normalize and print the final value.

Big Integer Generator Use Cases

  • Generating test data for big-number/BigInt-handling code
  • Stress-testing input validation that claims to support arbitrarily large integers
  • Producing sample large IDs or reference numbers for demos

Common Mistakes

  • Parsing the output with a regular floating-point Number, which silently loses precision above ~15-17 digits.
  • Requesting a digit length above the 100-digit cap.
  • Assuming generated values are prime or otherwise special; they're plain uniformly random digit strings.

Tips

  • Use a digit length just above your code's known 'safe integer' boundary to test where precision handling breaks.
  • Pair with the Integer Information Printer to inspect a single generated value's properties in detail.

References

Frequently Asked Questions