Generate one or more random numbers within any range. Free, instant, no signup required.
This free random number generator lets you produce one or more random numbers within any integer range — instantly, in your browser, with no account or download required. Set a minimum value, a maximum value, and the quantity you need, then click Generate. Results appear in under a second and can be copied with one click.
Whether you are running a lottery, picking a contest winner, rolling a virtual die, generating test data, or simply making a fair decision, a random number generator removes all bias from the process. Every number in your specified range has an exactly equal probability of being selected — there is no pattern, no weighting, and no repetition unless you generate enough numbers to naturally repeat.
This tool runs entirely in your browser. Nothing you enter is stored, logged, or sent to any server. You can use it offline once the page has loaded.
Using this tool takes under ten seconds. Here is the step-by-step process:
That is the entire process. There is no loading time, no CAPTCHA, no email required, and no limit on how many times you can use it.
This is the most commonly used range. To generate a random number between 1 and 100, leave the minimum at 1, set the maximum to 100, and click Generate. This range is widely used for:
The 1–10 range mimics a standard single die with extra faces, or a simple scoring scale. Common uses include:
To generate a random number between 1 and 10, set the minimum to 1, the maximum to 10, and click Generate.
A 1–50 range is useful when you have exactly 50 participants in a draw, or when you want a midpoint range that is smaller than 1–100. It is also common for lottery scratch cards and classroom quizzes. Set minimum to 1, maximum to 50.
The 1–20 range exactly matches a 20-sided die (d20), commonly used in tabletop role-playing games like Dungeons & Dragons. It is also useful for any draw involving up to 20 participants. Set minimum to 1, maximum to 20.
A range of 1–6 simulates a standard six-sided die. Use this whenever you need a virtual dice roll — for board games, teaching probability, or any situation where you would normally throw a physical die. Set minimum to 1, maximum to 6.
For larger lotteries, sweepstakes, or any use case with more than 100 participants, the 1–1000 range is ideal. You can also generate multiple numbers in this range to simulate drawing 6 numbers from a pool of 1,000. Set minimum to 1, maximum to 1000.
For probability and statistics demonstrations, the 0–1 range produces either 0 or 1 — essentially a random coin flip (heads or tails). This is also useful for binary random decisions. Set minimum to 0, maximum to 1.
A random number generator (RNG) is an algorithm or device that produces a sequence of numbers that has no discernible pattern and cannot be predicted in advance. The defining characteristic of a truly random sequence is that knowing all the previous numbers gives you zero information about what the next number will be.
Random number generators fall into two broad categories: hardware random number generators (HRNGs) and pseudo-random number generators (PRNGs). Understanding the difference helps you choose the right tool for your use case.
A hardware random number generator derives randomness from physical processes — thermal noise in a resistor, radioactive decay, atmospheric noise, or quantum effects. These sources are inherently unpredictable because they are governed by physical laws that cannot be perfectly modelled or predicted. True random number generators are used in cryptography, high-security systems, and scientific research where the quality of randomness has real consequences.
The tradeoff is speed and cost. Hardware RNGs are slower to produce numbers and require specialised hardware. For the vast majority of everyday use cases — games, lotteries, selecting a winner, generating test data — true randomness from a hardware source is not necessary and adds no practical benefit.
A pseudo-random number generator uses a deterministic algorithm to produce a sequence of numbers that behaves statistically like a random sequence. It starts from a "seed" — typically the current system time or another changing value — and runs it through a mathematical formula to produce the next number in the sequence.
PRNGs are fast, computationally cheap, and produce output that is statistically indistinguishable from true randomness for all practical purposes. For games, lotteries, classroom activities, software testing, statistical sampling, and fair selection, a PRNG is entirely sufficient.
This online random number generator uses JavaScript's built-in Math.random() function, which is a high-quality PRNG seeded automatically by your browser. It produces uniformly distributed numbers, meaning every integer in your specified range has exactly equal probability of being selected.
When we say the output is uniformly distributed, we mean that if you generate 10,000 random numbers between 1 and 10, you would expect roughly 1,000 results for each digit. No number is more likely than another. This is the correct distribution for lotteries, fair draws, game mechanics, and any situation where equal probability is the goal.
Some specialised applications require non-uniform distributions — a bell curve (normal distribution) for simulating human behaviour, or an exponential distribution for modelling waiting times. For those use cases, a general-purpose online RNG is not the right tool. But for the overwhelming majority of everyday needs, uniform distribution is exactly what you want.
Below are the most common real-world applications of a random number generator, with specific instructions for each use case.
The fairest way to run a lottery or giveaway is to assign each participant a unique number, then use a random number generator to pick the winning number. Here is the exact process:
This method is transparent and verifiable — you can screenshot the result and share it publicly to demonstrate the draw was fair. It is used by social media giveaways, classroom raffles, prize draws, and competition organisers worldwide.
Games depend heavily on randomness to stay interesting and unpredictable. A random number generator can be used for:
Board game nights, escape room design, pen-and-paper RPGs, and educational games all rely on these mechanics regularly.
Teachers and educators use random number generators for:
Software developers and QA engineers use random numbers constantly:
In research and statistics, random sampling is essential to ensure that results are representative and unbiased. A random number generator is used to:
Academic researchers, market research firms, clinical trials, and government surveys all depend on random selection to maintain statistical validity.
Sometimes the simplest use of a random number generator is to make a fair, unbiased decision when you are stuck between options. Assign each option a number, set your range accordingly, and let the generator decide. This is useful for:
A random number picker and a number randomiser are essentially the same thing as a random number generator — the terminology varies by use case. "Random number picker" is most often used in lottery and giveaway contexts, where you are literally picking a winning number from a pool. "Number randomiser" is used in games and decision-making contexts, where you are randomising a value rather than picking from a predefined list.
This tool covers all three use cases. Whether you call it a random number generator, a random number picker, or a number randomiser, the underlying mechanism is identical: you define a range, specify a quantity, and receive statistically random output.
A random number picker selects from a numbered range. A name picker selects from a list of names you enter. If you need to pick a winner by name rather than by number, a name picker is a better tool. If your participants are already numbered — as they typically are in formal competitions, ticketed events, or numbered spreadsheets — a random number picker is faster and simpler.
This question comes up frequently. The honest answer: for almost every common use case, a pseudo-random number generator is not just good enough — it is practically indistinguishable from a true random number generator.
A pseudo-random number generator is sufficient for:
In all of these cases, the sequence produced by a PRNG passes every standard statistical test for randomness. No participant in a lottery can predict or influence the output. The results are fair.
A true random number generator (hardware-based) is required only when:
For everything else — including almost everything a typical person would use an online random number generator for — a browser-based PRNG is perfectly adequate.
This tool can generate up to 100 random numbers in a single operation. To generate multiple numbers, simply change the "How Many Numbers?" field to the quantity you need and click Generate.
Common multi-number use cases include:
All generated numbers are displayed as a comma-separated list and can be copied to your clipboard with a single click, ready to paste into a spreadsheet, document, or message.
By default, this generator allows duplicate numbers — the same number can appear more than once in a multi-number result. This is appropriate for dice rolls (rolling the same number twice is valid) and many other use cases.
If you need unique numbers with no duplicates — for example, a lottery where each number can only be drawn once — generate your numbers and remove any duplicates, or generate one number at a time and re-roll if you receive a repeat. A dedicated lottery number generator with duplicate prevention is a more specialised tool for this specific requirement.
This tool fully supports negative integers. To generate a random number that includes negative values, simply enter a negative number as the minimum value. For example:
Negative random numbers are used in physics simulations, financial modelling (positive and negative returns), temperature ranges spanning below zero, and certain game mechanics.
There is no upper limit on the range size this tool supports. You can set the minimum to 1 and the maximum to 1,000,000 if your application requires it. Large ranges are used for:
The problem of generating random numbers is older than computers. Physical methods — dice, shuffled cards, numbered balls in a drum — have been used for millennia in games, divination, and decision-making. The concept of randomness as a mathematical property was formalised by probability theorists in the 17th and 18th centuries, with foundational contributions from Pascal, Fermat, and Laplace.
The first algorithmic pseudo-random number generator was the Linear Congruential Generator (LCG), proposed by D. H. Lehmer in 1948. It works by multiplying the previous number by a constant, adding an offset, and taking the remainder when divided by a large prime — producing a sequence that cycles through a large number of values before repeating. LCGs are still used today in some applications due to their speed.
The Mersenne Twister, developed by Matsumoto and Nishimura in 1997, became the dominant PRNG for general-purpose use. It has a period of 2^19937 − 1 (a number with over 6,000 digits), passes all standard statistical tests for randomness, and is fast enough for real-time applications. Most programming languages — including the JavaScript runtime used by this tool — use the Mersenne Twister or a similar modern algorithm.
True hardware random number generators became practically important with the rise of internet cryptography in the 1990s. Generating a secure RSA key requires a source of randomness that cannot be predicted or reproduced, which ruled out PRNGs. Modern CPUs from Intel and AMD include hardware RNG instructions (RDRAND) that harvest entropy from thermal noise on the chip itself, making cryptographically secure random numbers available at the hardware level.
Set the minimum value to 1, the maximum value to 100, and click Generate. You'll get a random number from 1 to 100 instantly.
Yes. Set the minimum to 1, the maximum to 10, and click Generate. The tool works for any integer range — from very small ranges like 1–6 to very large ones like 1–1,000,000.
Set the minimum to 1 and the maximum to 50, then click Generate. This is useful for draws with up to 50 participants or any 50-value range you need.
This tool uses a pseudo-random number generator (JavaScript's Math.random()), which is sufficient for lotteries, games, giveaways, and testing. The output is statistically indistinguishable from true randomness in all practical applications. For cryptographic uses, a hardware-based true RNG is recommended.
Up to 100 numbers in a single operation. Enter the quantity in the "How Many Numbers?" field before clicking Generate.
Yes. Enter any integers as your minimum and maximum — positive, negative, or any size. The only requirement is that the minimum is less than the maximum.
Yes, by default. The same number can appear more than once when generating multiple results. This is correct behaviour for dice rolls and most general-purpose uses. If you need unique numbers with no repeats, generate one at a time and skip any duplicates.
Yes. Assign each lottery participant a unique number, set the range to match your total participant count, and generate as many numbers as you have prizes. The result is a fair, verifiable random draw.
Yes, completely free. There is no account, no subscription, no download, and no usage limit.
No. Everything runs in your browser. No data is sent to any server, and nothing is logged or stored.
Yes. Set the range to match your die type: 1–6 for a standard d6, 1–4 for a d4, 1–8 for a d8, 1–10 for a d10, 1–12 for a d12, or 1–20 for a d20. Set the quantity to the number of dice you want to roll simultaneously.
They are effectively the same tool. "Random number generator" is the general technical term. "Random number picker" is the same concept described from a practical, user-facing perspective — the idea of picking one number from a defined range. Both operate on the same principle.