🔢 LLM Token Counter
Instantly see the estimated token count of your text for GPT and Claude — calibrated against OpenAI tiktoken. Everything runs in your browser; your text never leaves this page.
GPT-4o / GPT-4.1 / o-series (o200k_base encoding). Within ~±10% of OpenAI tiktoken.
Edit the price for your own model (e.g. GPT-4o input ≈ $2.50/1M). All figures are approximate and cover input tokens only.
What is a Token Counter (LLM Token Estimator)?
A token counter is a free tool that estimates how many tokens a piece of text will be split into by large language models (LLMs). Models like GPT, Claude and Llama do not read text character by character — they process it as sub-word pieces. This splitting step is called tokenization and is usually done with the BPE (byte pair encoding) algorithm. Because your API cost, whether you fit inside the model's context window, and response latency all depend on the token count, estimating tokens is a critical step for anyone building with AI. Every calculation in this tool runs in your browser; the text you type is never sent to a server.
How it works (transparent method)
This tool does not download megabytes of BPE vocabulary that would slow the page down. Instead it splits the text into pieces (words, numbers, punctuation and whitespace) using a pattern close to the pre-tokenization regex that real GPT tokenizers use, then combines the piece count with the UTF-8 byte count in a linear formula calibrated against OpenAI's official tiktoken library (o200k_base and cl100k_base): tokens ≈ a·pieces + b·bytes. The coefficients (a, b) were fit to real tiktoken output over English, Turkish and code samples. Because UTF-8 bytes are used, accented characters (e.g. Turkish ç, ğ, ı, ö, ş, ü — two bytes each) automatically reflect their higher token cost.
How accurate is it?
For GPT models the estimate is usually within about 10% of the real tiktoken count (o200k_base is more accurate; cl100k_base drifts a bit more on non-English text). Claude is different: Anthropic does not publish a public tokenizer, so the Claude figure here is only a rough estimate. An exact Claude token count is available only from Anthropic's messages/count_tokens API (which needs the network and an API key, so this local tool does not use it). Note: tiktoken, which is correct for GPT, should not be used for Claude — it miscounts Claude tokens by 15–20%. Always trust the provider's official count for billing decisions.
Example
The sentence "Large language models read text as tokens." is estimated at about 10 tokens for GPT-4o (o200k); the real tiktoken count is 8, so the estimate is in the right ballpark. At $2.50 per 1M input tokens that is ≈ $0.000025 — letting you see the rough cost of a call before you ever hit the API.
Tips and realistic expectations
- It is an estimate. The real count varies by model, tokenizer version, whitespace, emoji and code. Always trust the provider's official count for billing.
- English ≈ 4 chars/token is an average; code, URLs, numbers and rare words break that ratio.
- Turkish and other non-English languages usually consume more tokens per word — leave headroom when you budget.
- Both input and output tokens are billed. This tool only estimates the text you enter (input); the model's reply is counted separately.
Frequently Asked Questions
Is the token count exact?
For GPT models it is close: the tool splits text into pieces and combines the piece count with UTF-8 bytes in a formula calibrated against OpenAI tiktoken, landing within about 10% of the real count. For Claude the result is a rough estimate (there is no public tokenizer). Always use the provider's official count for billing.
What is a token and why does it matter?
A token is the smallest unit a language model uses to process text — usually a sub-word piece smaller than a whole word. In English one token averages ~4 characters. Tokens matter because API cost, context-window limits and latency all depend on the token count.
Why do non-English texts use more tokens?
Tokenizers are trained mainly on English, so words in agglutinative languages like Turkish get split into more sub-word pieces. Accented characters and long suffix chains raise the tokens-per-word ratio.
Is my text sent to a server?
No. The tool runs entirely in your browser; there are no API calls and your text is never uploaded.