Free tool
Estimate your prompt's token count
Paste any text and get an instant estimated token count for GPT, Claude, and Gemini. Free, no signup.
Free, no signup. This is an estimate based on OpenAI's published rule of thumb, not an exact tokenizer — actual counts vary slightly by model.
How it works
Paste your prompt
Drop in a prompt, document, or any text you're planning to send to an AI API.
Get an instant estimate
The tool applies the standard rule-of-thumb ratio of characters and words per token to estimate a count.
Plan your usage or cost
Use the estimate to check against a model's context window or feed it into the API Cost Calculator for a price estimate.
Why use this tool
No account or API key needed
Get an estimate without signing up for an API key just to check token usage.
Works for any model family
The estimate is a reasonable approximation across GPT, Claude, and Gemini, even though each uses its own exact tokenizer.
Instant, live estimate
The count updates as you type or paste, no button to click.
Clearly labeled as an estimate
The tool is upfront that this is an approximation, not an exact tokenizer, so you know how much to trust the number.
Free, no signup, no limit
Estimate as many prompts as you want with no account and no cap.
Nothing you paste is stored
The estimate is calculated entirely in your browser — nothing is saved or sent anywhere.
When you'd actually use this
Checking if a prompt fits a context window
Get a rough token count before submitting a long document or conversation history to an API with a fixed context limit.
Estimating API costs before running a job
Combine the token estimate with the API Cost Calculator to project cost before running a large batch job.
Comparing prompt lengths across drafts
See how much a prompt shrinks after trimming filler text or restructuring instructions.
Understanding why a request got truncated
A response cut off mid-sentence often means the combined input and output token count hit the model's limit.
Budgeting a fine-tuning or batch dataset
Estimate total tokens across a dataset before starting a fine-tuning job priced by token volume.
Tips for best results
- 1Treat the estimate as a range, not an exact number — actual tokenization can vary a few percent depending on the model and language.
- 2Code and non-English text often tokenize less efficiently than plain English prose — expect a higher token count than the word count alone would suggest.
- 3When budgeting for an API call, remember both the input prompt and the expected output count toward the total token cost.
- 4For an exact count before a production run, use your provider's own tokenizer library — this estimate is best for quick, upfront planning.
Frequently asked questions
What is a token in AI models?
A token is a chunk of text a language model processes — roughly a word or part of a word for English, though exact splitting depends on the model's specific tokenizer. Pricing and context limits for most AI APIs are measured in tokens, not words or characters.
How accurate is this token count?
It's an estimate based on OpenAI's published rule of thumb (roughly 4 characters or 0.75 words per token for English), not an exact tokenizer — real counts vary a few percent by model and can differ more for non-English text or code.
Why do different AI models count tokens differently?
Each model family uses its own tokenizer trained on its own vocabulary, so the same sentence can split into a different number of tokens depending on which model processes it — this is exactly why Claude Sonnet 5's new tokenizer changed effective costs even at the same per-token price.
Does this tool store what I paste?
No — the estimate is calculated entirely in your browser and nothing you paste is saved or sent anywhere.
Why does code tokenize differently than plain text?
Code has more punctuation, whitespace, and unusual character sequences than natural language, which typically causes it to split into more tokens per character than plain English prose.
Does the token count include both my prompt and the AI's response?
This tool only estimates the text you paste in — remember that API pricing and context limits count both your input prompt and the model's generated output together.
Is a higher token count always worse?
Not inherently — it just means higher cost and more of the context window used. A verbose but well-structured prompt with more tokens can still outperform a terse one if the extra content improves the response quality.
Should I use this instead of my provider's official tokenizer?
Use this for a fast, no-signup ballpark estimate during planning — for an exact count before a cost-sensitive production run, use your provider's own tokenizer library or API response metadata.