This LLM token counter counts the tokens in any text using OpenAI’s real o200k tokenizer, plus a cost estimate across models. It’s useful for fitting prompts into a context window and budgeting API spend. Everything runs in your browser — nothing you paste is uploaded.
Because models price and limit usage by tokens rather than words, an accurate token count is the only reliable way to predict cost and avoid hitting context limits.
What is a token?
A token is the unit a language model reads and generates. In English it’s roughly three-quarters of a word, or about four characters. Common words are a single token; rarer words, code and punctuation split into several. So 1,000 words is usually around 1,300 tokens.
Why token counts matter
Every API charges per token and every model has a maximum context window measured in tokens. Counting tokens before you send a request tells you whether your prompt will fit, how much the call will cost, and how much room is left for the response.
Exact vs approximate counts
The GPT figure here is exact — it uses the same o200k_base tokenizer that GPT-4.1 and GPT-5-class models use. Anthropic and Google don’t publish a browser tokenizer, so their counts are shown as a clearly-labelled four-characters-per-token estimate. For an exact Claude count, use Anthropic’s count_tokens API.