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.