About this tool
Count AI prompt tokens locally
Use this AI token counter to size prompts before sending them to GPT, Claude, Gemini, or similar large language models. GPT-style counts use a self-hosted tokenizer in the browser; Claude and Gemini counts are labeled estimates because exact counts require provider APIs.
Supported inputs and outputs
Plain text, prompts, chat transcripts, markdown, code blocks, UTF-8 byte counts, word counts, line counts, GPT o200k tokens, GPT cl100k tokens, Claude estimates, Gemini estimates, and context-window usage.
Privacy note
Token Counter for GPT, Claude, and Gemini runs in your browser. DataZier does not upload your pasted text or selected files for processing.
What are AI tokens?
Tokens are the chunks of text that language models read and generate. A token can be a whole word, part of a word, punctuation, whitespace, an emoji, or a piece of code syntax. Models do not usually count text by words or characters; they count by the tokenizer used by that model family.
How token counting works
A tokenizer splits your text into repeatable pieces from a model vocabulary. Common English words often become one token, while rare words, long identifiers, non-English text, JSON, markdown, and source code may split into more tokens. That is why two prompts with the same word count can have different token counts.
Why token counts matter
Token counts affect whether a prompt fits inside a model context window, how much room remains for the answer, and often how much an API request costs. If a prompt is too large, you may need to shorten instructions, remove duplicated context, summarize source material, or split work into multiple calls.
GPT vs Claude vs Gemini tokens
Different AI providers use different tokenizers, so the same text can produce different counts across GPT, Claude, and Gemini-style models. This tool uses self-hosted GPT tokenizer bundles for exact GPT-style counts and local estimates for Claude and Gemini because exact Claude and Gemini counts require provider API calls.
Tokens are not the same as words
As a rough mental model, English prose is often around three to five characters per token, but that shortcut breaks down for code, tables, compressed JSON, emojis, URLs, mixed languages, and unusual formatting. Use the live token count rather than a word-count rule when context size matters.
How to reduce token usage
Remove repeated instructions, trim pasted logs, collapse large tables, replace verbose examples with one representative sample, summarize source documents before reuse, and keep only the fields the model needs. For structured data, minifying JSON can reduce whitespace but may make debugging harder.