Is this truly random?

The generator uses the browser's crypto.getRandomValues API when available, which provides cryptographically strong random numbers. For non-secure contexts (older browsers) it falls back to Math.random, which is pseudo-random.

Can I generate without duplicates?

Yes — check the 'unique values' option and the generator will return a random sample without replacement.

Can I use it for a raffle or giveaway?

For informal raffles, yes. For regulated giveaways (government lotteries, etc.), you likely need a certified RNG with audit logs — this tool doesn't provide those.

What's the max range?

Safe integer range: -9,007,199,254,740,991 to 9,007,199,254,740,991. Beyond that, JavaScript loses precision.

Why does the same range produce different results each click?

That's the point — each generation calls a fresh random source. If you want REPRODUCIBLE random numbers (same seed → same output), use a seeded RNG (Mulberry32, xorshift, or Math.seedrandom library). Browser-native randomness can't be seeded (security feature). Reproducible randomness is useful for: generating test data with known values, replaying simulations, debugging behavior triggered by specific number sequences. For lottery / raffle use, fresh randomness is what you want.

Can I generate random numbers following a specific distribution?

Default is uniform distribution (every number equally likely). For Gaussian/normal distribution (mean + std deviation), use Box-Muller transform: take two uniform samples u1, u2 in [0,1], compute z = sqrt(-2*ln(u1)) * cos(2*pi*u2), result is normally distributed with mean 0 and std deviation 1. For Poisson, exponential, log-normal: each has its own transformation from uniform. For most casual uses, uniform is right; non-uniform distributions matter for simulation, statistics, and machine-learning data generation.

Random & Fun · Free tool

Random Number Generator

Generate random numbers in any range with or without duplicates. Perfect for draws and testing — this free online tool works instantly with no signup.

Updated June 2026

MinMaxHow manyUnique only

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What it does

A free random number generator. Set min, max, count, and whether results must be unique. Uses the browser’s crypto.getRandomValues API where available (cryptographically strong), falling back to Math.random for older environments. Suitable for everyday raffles, games, sampling, and decision randomness; for security-critical applications, use a dedicated crypto library.

Useful for raffles and giveaways, classroom random sampling (call on student #14), dice replacement (1-6, 1-20), test data generation, statistical sampling without replacement (toggle ‘unique’ to draw from a range without repeats), and decision-by-randomness when options are equivalent. The unique-values mode does random-without-replacement: from range 1-100 picking 5, you’ll get 5 distinct numbers in random order.

Range is up to JavaScript’s safe integer range: -9,007,199,254,740,991 to 9,007,199,254,740,991 (about 9 quadrillion either side of zero). For larger ranges, use BigInt-aware tools. Decimal numbers are supported with adjustable precision. Note: random number quality matters for some applications; certified randomness for regulated lotteries requires audit logs and certified RNG hardware that this tool doesn’t provide. For cryptographic randomness, use our UUID generator instead.

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How to use it

Set min and max values.
Set how many numbers to generate.
Toggle unique-only if needed.
Click Generate — copy or regenerate.

When to use this tool

Picking raffle / giveaway winners from a numbered list.
Generating test data for a database or API mock.
Random sampling for surveys (pick 50 random respondents from 5,000).
Replacing dice for tabletop games or simple game mechanics.

When not to use it

Cryptographic key generation — use a security-focused crypto library.
Government-regulated lotteries — those require certified RNG with audit logs.
Reproducible test seeds — this RNG can’t be seeded; use a seedable PRNG library instead.

Common use cases

Raffle drawer for a 200-ticket fundraiser picks numbers 1-200 without repeats.
Teacher generating attendance-call order for a class of 30.
Survey researcher samples 100 random IDs from a database of 10,000 users.
Game designer rolling 1d20 for character checks during a tabletop session.

Frequently asked questions

Is this truly random?: The generator uses the browser's crypto.getRandomValues API when available, which provides cryptographically strong random numbers. For non-secure contexts (older browsers) it falls back to Math.random, which is pseudo-random.
Can I generate without duplicates?: Yes — check the 'unique values' option and the generator will return a random sample without replacement.
Can I use it for a raffle or giveaway?: For informal raffles, yes. For regulated giveaways (government lotteries, etc.), you likely need a certified RNG with audit logs — this tool doesn't provide those.
What's the max range?: Safe integer range: -9,007,199,254,740,991 to 9,007,199,254,740,991. Beyond that, JavaScript loses precision.
Why does the same range produce different results each click?: That's the point — each generation calls a fresh random source. If you want REPRODUCIBLE random numbers (same seed → same output), use a seeded RNG (Mulberry32, xorshift, or Math.seedrandom library). Browser-native randomness can't be seeded (security feature). Reproducible randomness is useful for: generating test data with known values, replaying simulations, debugging behavior triggered by specific number sequences. For lottery / raffle use, fresh randomness is what you want.
Can I generate random numbers following a specific distribution?: Default is uniform distribution (every number equally likely). For Gaussian/normal distribution (mean + std deviation), use Box-Muller transform: take two uniform samples u1, u2 in [0,1], compute z = sqrt(-2*ln(u1)) * cos(2*pi*u2), result is normally distributed with mean 0 and std deviation 1. For Poisson, exponential, log-normal: each has its own transformation from uniform. For most casual uses, uniform is right; non-uniform distributions matter for simulation, statistics, and machine-learning data generation.

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