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LLM Token Counter is a free, in-browser tokenizer that gives you exact tiktoken counts for OpenAI (GPT-4o, GPT-4, GPT-3.5, o1, o3) and accurate estimates for Claude, Llama 3, Gemini, and Mistral. See your prompt tokenized live with color-coded chunks, character/word/sentence/line counts, average chars-per-token per model, and sample prompts for quick testing. 100% client-side, no signup, no API key, no data leaves your browser. Works offline after first load. Free forever.
Here's a strong first comment, tuned for Product Hunt's conventions (origin story + features + clear feedback ask). It runs ~290 words — the sweet spot for maker comments.
Recommended first comment
Hey Product Hunt!
I built LLM Token Counter because I kept hitting the same annoying
problem: every time I needed to estimate tokens for a prompt, I had to
either spin up the OpenAI Playground or paste my prompt into some random
tokenizer site I did not trust with my data.
So I built one that:
- Gives you exact tiktoken counts for GPT-4o, GPT-4.1, GPT-4, GPT-3.5,
o1, and o3 using the real OpenAI tokenizer (cl100k_base + o200k_base)
compiled to WASM
- Provides accurate estimates for Claude, Llama 3, Gemini, and Mistral
using each provider's published chars-per-token ratios (typically
within 5-10% of real counts)
- Visualizes your prompt live with color-coded chunks so you can see
exactly how tokenization splits your text
- Shows character, word, sentence, and line counts side by side
- Includes a reference table of chars-per-token per model and built-in
sample prompts (short, system prompt, code, long passage)
Critically, everything runs 100% in your browser. No backend, no API key,
no data leaves your device. Works offline after the first load.
It is part of a free dev tools collection I maintain at singhajit.com/tools
— 27 tools including JWT decoder, SQL formatter, regex tester, cron
generator, snowflake decoder, and more. All client-side, all free, all
without signup.
What I would love feedback on:
- Models I should add next (I am considering Mixtral 8x22B, DeepSeek V3,
Qwen)
- Are the chars-per-token estimates for non-OpenAI models accurate enough
for your workflow, or do you need exact counts?
- Any UX rough edges
Thanks for checking it out!
— Ajit
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About LLM Token Counter on Product Hunt
“Estimate prompt cost before you call the LLM API”
LLM Token Counter was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #144 on the daily leaderboard. LLM Token Counter is a free, in-browser tokenizer that gives you exact tiktoken counts for OpenAI (GPT-4o, GPT-4, GPT-3.5, o1, o3) and accurate estimates for Claude, Llama 3, Gemini, and Mistral. See your prompt tokenized live with color-coded chunks, character/word/sentence/line counts, average chars-per-token per model, and sample prompts for quick testing. 100% client-side, no signup, no API key, no data leaves your browser. Works offline after first load. Free forever.
LLM Token Counter was featured in Productivity (653.8k followers), Developer Tools (514k followers) and Artificial Intelligence (471k followers) on Product Hunt. Together, these topics include over 311.3k products, making this a competitive space to launch in.
Who hunted LLM Token Counter?
LLM Token Counter was hunted by Ajit Singh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how LLM Token Counter stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Ajit Singh