Free AI Tokens Counter

What Is gpt-tokenizer and Why Token Counting Matters

This free AI token counter is powered by gpt-tokenizer, the fastest and most lightweight GPT tokenizer for JavaScript. It is a production-grade TypeScript port of OpenAI's tiktoken library, widely adopted in enterprise applications and open-source projects. It supports every OpenAI encoding and model — including GPT-4o, GPT-4.1, o1, o3, o4-mini, and the latest GPT-5 — giving you exact token counts that match what the API actually charges.

Token counting is essential when working with AI APIs. Every API call to ChatGPT, Claude, or other LLMs is billed by tokens — not words or characters. A single word can be one token or several, depending on the model's encoding. This tool lets you check exactly how many tokens your prompt uses before you send it, helping you stay within context window limits and estimate API costs accurately.

The tokenizer supports all OpenAI encodings (cl100k_base, o200k_base, o200k_harmony, r50k_base, p50k_base, and p50k_edit), so whether you are building prompts for GPT-3.5, GPT-4, or the newest models, the count will match what the API actually charges you. It runs entirely in your browser with no data sent to any server.

How Token Counting Works Under the Hood

If you need tokenization in JavaScript or TypeScript, gpt-tokenizer is the leading alternative to running tiktoken via WASM or Python bindings. Available on npm, it works in any JS runtime — browser, Node.js, Deno, and Bun. It is the fastest tokenizer on npm, outperforming even WASM and native binding implementations, with the smallest bundle size thanks to compact encoding storage and tree-shakeable per-model imports.

The API includes encode, decode, countTokens, isWithinTokenLimit, encodeChat for chat-format messages, and estimateCost with built-in model pricing data. It supports streaming tokenization via generators, synchronous loading, and an LRU merge cache for repeated tokenization. There are no global caches, so no memory leaks — making it production-safe for long-running server applications.

How to Count Tokens for ChatGPT, Claude, and Other AI Models

Whether you are building a chatbot, writing system prompts, or managing RAG pipelines, knowing how many tokens your text consumes is critical. Every AI API — OpenAI, Anthropic, Google, Mistral — bills by tokens, and every model has a context window limit measured in tokens. Sending a prompt that exceeds the limit causes errors or silent truncation; underestimating token usage leads to surprise costs on your monthly bill.

This tool gives you the exact count for any OpenAI model by using the same BPE encoding the API uses internally. For Claude, Gemini, Llama, and other models, the count is a reliable estimate because modern tokenizers share similar vocabulary sizes and merge strategies. Paste your prompt, system message, few-shot examples, or even a full document and see the token count in real time — no API key, no rate limits, and no data leaving your browser.

Where token counting fits into real AI development work

Building a RAG pipeline means deciding how large each document chunk should be, and that decision is fundamentally a token budget problem: chunks need to be large enough to carry useful context but small enough that several of them plus the system prompt still fit comfortably inside the model's context window, a number you can only get right by actually counting tokens on real chunk samples rather than guessing from word count. Prompt engineers comparing two versions of a system prompt paste each one in here to see the exact token delta, since a rewording that reads shorter to a human does not always tokenize shorter, verbose phrasing sometimes compresses into fewer BPE tokens than a terse rewrite full of rare words.

Teams preparing a fine-tuning dataset check that individual training examples stay under the model's per-example token limit before submitting a job, since a dataset with even a small percentage of oversized examples can cause a fine-tuning run to fail partway through or silently truncate the exact examples that mattered most.

Why BPE became the standard way to tokenize text for language models

Before Byte Pair Encoding became standard, language models faced a hard tradeoff: word-level tokenization needed an enormous vocabulary to cover every possible word and still broke on any word it had never seen during training, while character-level tokenization avoided that problem but produced extremely long sequences that made training and inference far more expensive. BPE, adapted for language modeling and popularized in OpenAI's GPT-2 paper, solves this by starting from individual bytes and iteratively merging the most frequent adjacent pairs into a fixed vocabulary of sub-word units, common words end up as single tokens while rare words and unfamiliar text degrade gracefully into smaller familiar pieces instead of breaking entirely.

That graceful degradation is exactly why a made-up product name, a typo, or a string of code still tokenizes into something sensible rather than crashing the pipeline, the model has simply never seen "xqzloop" as a whole word, but it has seen the sub-word pieces that compose it. This is also why token count is not simply proportional to character count: common English prose tokenizes efficiently because BPE's vocabulary was built from massive amounts of exactly that kind of text, while code, rare technical terms, and non-Latin scripts tokenize less efficiently because the merge rules had comparatively less of that content to learn from.

When this browser tool beats running tiktoken yourself

For token counting inside an actual application, calling tiktoken directly in Python or gpt-tokenizer in a Node.js backend is still the right approach, since you need programmatic counts integrated into real code, not a one-off number. But for the extremely common case of just wanting to know "how many tokens is this," while drafting a prompt, sizing a document before deciding how to chunk it, or sanity-checking a number before writing any code at all, opening a browser tab and pasting text is faster than starting a Python REPL, installing a package, and writing three lines just to get an answer you needed thirty seconds ago.

It is also useful as a cross-check: if a number returned by your own tiktoken integration looks surprising, pasting the same text here confirms whether the issue is the text itself or a bug in how your code called the tokenizer, since this tool uses the identical BPE encoding and merge rules that tiktoken and gpt-tokenizer both implement.