Random Number Generator
Generate random numbers within a range.
Generate random numbers within a range.
This free Random Number Generator from KX Toolkit is part of our all-in-one online toolkit. It runs entirely in your browser, so your data never leaves your device for client-side operations. 100% free, forever - no paywall, no credit card, no trial.
How to use the Random Number Generator
- Pick the algorithm or generation options.
- Enter your input (or click "Generate" for random output).
- Click the action button - the result appears instantly.
- Copy the result. Never paste sensitive secrets into the input again afterwards.
What you can do with the Random Number Generator
- Generate strong passwords for new accounts.
- Hash data for verifying file integrity.
- Encode binary in Base64 for inline embedding.
- Test bcrypt or MD5 outputs while building auth systems.
Why use KX Toolkit's Random Number Generator
- Browser-based: Works on Windows, macOS, Linux, iOS and Android - no install, no extension.
- Privacy-first: Client-side tools never upload your data; server-side tools delete files right after processing.
- Mobile-friendly: Full feature parity on phones and tablets - not a stripped-down view.
- Fast: Optimised for instant feedback. No artificial waiting screens, no email-gated downloads.
- One hub for everything: 300+ tools across SEO, text, image, PDF, code, color, calculators and more - skip switching between sites.
Tips for the best results
Always check the URL bar before pasting sensitive data - KX Toolkit's crypto tools run client-side, but you should still verify you're on the right domain.
Related Password & Encryption
If you find this tool useful, explore the full Password & Encryption collection or browse our complete tool directory. KX Toolkit is built for marketers, developers, designers, students and anyone who needs a quick utility without signing up for yet another SaaS.
Are the numbers truly random?
They are cryptographically secure pseudorandom numbers from your browser's Web Crypto API, which seeds itself from operating system entropy sources like hardware noise and system timers. For practical purposes - games, contests, simulations, sampling - the output is indistinguishable from true randomness. For high-stakes cryptographic key generation you would use the same underlying API, so the numbers are suitable for almost any use you are likely to have.
How does the min-max range affect the result?
The generator picks a number greater than or equal to the minimum and less than or equal to the maximum, inclusive on both ends. Larger ranges have more possible outputs but each individual value remains equally likely. If you choose a range whose size is not a power of two, the implementation uses rejection sampling to avoid the modulo bias that naive approaches introduce.
Can I use this for a giveaway or contest?
Yes, for casual draws among friends or community contests it is more than sufficient. For regulated lotteries or anything legally binding, follow the rules of your jurisdiction - they often require an auditable, certified RNG and witnessed draws. For corporate giveaways, take a screen recording of the draw and document the inputs (entry list, range, seed if any) so the result is reproducible and reviewable.
Is the result generated locally or on a server?
Locally, in your browser. The tool calls the Web Crypto API's getRandomValues directly, so the random bytes are produced on your device using OS-level entropy. Nothing about the request, the range, or the result leaves your machine. This also means each tab is independent - refreshing or opening a new window starts a fresh entropy stream rather than continuing a server-side sequence.
How do I generate a sequence of unique random numbers?
Generate one number, then exclude it from the range and generate the next. For small batches a simple approach is to build a list of all possible values and shuffle them with the Fisher-Yates algorithm, then take as many as you need from the front. The generator targets one number at a time, but you can run it repeatedly and discard duplicates if the range is large compared to the count.
Why are some random results biased on simple modulo implementations?
If you take a 32-bit random integer and apply modulo against a range that does not divide evenly into 2^32, the lower remainders appear slightly more often than the higher ones. Over a million draws this skew is detectable. Good generators detect when a candidate falls in the biased tail and discard it, retrying until a clean value is produced. This tool uses that rejection-sampling approach.
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