AI Token Counter
Count AI tokens, words, characters, and context usage directly in your browser. Use general estimates or exact local OpenAI tokenizer modes without sending your text to a MarkForge server.
Your text is processed locally in your browser and is never uploaded for token counting.
Token Counting Method
Release to load file
Accepted uploads: .txt or .md, up to 5 MB. Drag a file onto the text area, or use Upload File.
Estimated Tokens
0
General AI Estimate
Counting…
Statistics
Context Window Usage
Enter a whole number greater than 0.
0 / 8,192 tokens
0.0% of selected context window
This compares only the text entered here with the context size you selected. System prompts, conversation history, tools, attachments, generated output, and provider-specific overhead may also use context.
Text too large for your target context window?
Split it into structured Markdown chunks with MarkForge.
Open Markdown Chunker →What Is an AI Token Counter?
AI models process text as tokens rather than simply counting words. A token may represent a word, part of a word, punctuation, whitespace, or another text fragment depending on the tokenizer. An AI token counter helps estimate or calculate how much tokenized text a prompt, Markdown file, document, or other content contains.
MarkForge counts or estimates tokens in pasted text and can compare that result with a selected context-window size, making it useful as both an AI token counter and a token calculator — all running locally in your browser.
New to tokenization? Learn what AI tokens are and how tokenization works in the MarkForge guide.
How to Use the MarkForge AI Token Counter
Step 1
Paste or upload your text
Paste text or Markdown directly, or upload a .txt or .md file.
Step 2
Choose a counting method
Use the General AI Estimate, or select an OpenAI o200k_base or cl100k_base encoding.
Step 3
Review tokens and context-window usage
See the token count, text statistics, and how much of a selected context window your text would use.
Estimated vs. Exact Token Counts
The General AI Estimate is a rough, model-neutral planning estimate based on text length. It is useful for a quick sense of scale across any AI model, but it is not tied to any specific tokenizer.
The OpenAI encoding modes (o200k_base and cl100k_base) tokenize your exact text using the selected local encoding, so the token count reflects how that specific tokenizer would split the text you entered.
Different AI providers and models use different tokenizers, so counts can vary between them. In addition, a full API request may include additional tokens beyond the visible text you typed here — such as system messages, chat formatting, tool definitions, or attachments.
Why AI Token Counts Matter
Most AI models process input and output within a fixed context window measured in tokens. Understanding roughly how many tokens your text uses can help you plan prompts, prepare documents, and work within a model's context limits.
Token counting is also useful for retrieval-augmented generation (RAG) workflows and document chunking, where content is split into pieces sized to fit a model's context window, and for avoiding unnecessarily large prompts that use more context than a task requires.
If a Markdown document is too large for your planned workflow, use the Markdown Chunker to split it into smaller sections. You can also clean or preview Markdown before counting tokens.
This tool provides planning estimates and local encoding counts; it does not calculate or guarantee API costs or usage, which depend on the provider, model, and pricing in effect at the time of a request.
AI Tokens vs. Words
A token count is not the same as a word count. Tokenizers frequently split a single word into multiple tokens, combine short words or punctuation into one token, or treat whitespace as part of a token, depending on the language and the specific tokenizer design.
Because of this, the same piece of text can have a noticeably different token count than word count, and that ratio can shift with punctuation, formatting, and language.
Private by Design
MarkForge performs counting locally in the browser. Pasted text and uploaded Markdown/text files are not sent to a MarkForge token-counting server, and no tokenizer mode on this page calls an external AI provider or API.
For additional details, see the MarkForge Privacy Policy.
Related Tools
PDF to Markdown Converter →
Convert PDF files into clean, structured Markdown directly in your browser.
DOCX to Markdown Converter →
Convert Microsoft Word DOCX files into clean Markdown directly in your browser.
HTML to Markdown Converter →
Convert HTML source or HTML files into clean Markdown directly in your browser.
Markdown Previewer →
Write or paste Markdown and see a live formatted preview in your browser.
Markdown Chunker →
Split large Markdown documents into structured chunks for AI, RAG, and knowledge-base workflows.
Markdown Cleaner →
Clean and normalize Markdown formatting while preserving document structure.
Frequently Asked Questions
What is an AI token?
An AI token is a unit of text that a language model reads or generates. Depending on the tokenizer, a token can be a whole word, part of a word, a punctuation mark, whitespace, or another text fragment.
How are AI tokens calculated?
Tokenizers use trained rules, commonly byte-pair encoding (BPE), to split text into a sequence of tokens from a fixed vocabulary. The exact split depends on the tokenizer's vocabulary and rules, which is why different tokenizers can produce different counts for the same text.
Is one AI token the same as one word?
No. A token is often shorter than a word — many words split into two or more tokens, while some short words or punctuation marks combine into a single token. Token count and word count are usually different numbers.
How accurate is the General AI Estimate?
The General AI Estimate is a rough, model-neutral planning figure based on roughly one token per four characters of text. It is meant for quick, cross-model planning, not as an exact count for any particular tokenizer.
Why do different AI models have different token counts?
Different providers and model families use different tokenizers with different vocabularies. The same text can therefore produce a different token count depending on which model or encoding is used to process it.
Can I use this token counter for ChatGPT or OpenAI models?
Yes. The o200k_base and cl100k_base modes count your pasted text using the selected local OpenAI-compatible encoding. A full ChatGPT or API request can include additional tokens beyond the visible text, such as system messages, chat formatting, tools, or attachments.
Can I use this token counter for Claude?
MarkForge does not include an exact Claude tokenizer. Use the General AI Estimate for a model-neutral planning figure; exact Claude tokenization can differ from this estimate.
Can I use this token counter for Gemini?
MarkForge does not include an exact Gemini tokenizer. Use the General AI Estimate for a model-neutral planning figure; exact Gemini tokenization can differ from this estimate.
Does MarkForge count tokens exactly for every AI model?
No. Tokenization varies by model and provider. MarkForge provides exact local counts for its supported OpenAI tokenizer encodings and a General AI Estimate for broader planning.
Does MarkForge upload my text?
No. Pasted text and uploaded files are counted locally in your browser and are not uploaded to a MarkForge server or any third-party API for token counting.
Can I upload a Markdown file?
Yes. MarkForge accepts .txt and .md files up to 5 MB, read entirely in your browser using the File API.
What is a context window?
A context window is the maximum number of tokens a model can take into account at once across the input and, in many cases, the output of a request. The Context Window Usage section lets you compare your text's token count against a context size you select.
Why can the final API token usage differ from the number shown here?
This tool counts only the text you pasted or uploaded. A real API request can add tokens for system prompts, conversation history, tool or function definitions, attachments, formatting, and other provider-specific overhead, so actual usage can be higher than the number shown here.