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Token Counter

Estimate token counts for text (approximate GPT/Claude tokenization) to manage context length and cost.

Tokens

0

Chars

0

Words

0

Context estimate (GPT-4 class 128K):

How to use: 3 simple steps

Step 1

Enter content

Paste or type text.

Step 2

Process

Click to generate results.

Step 3

Copy & use

Copy the result for your use case.

Key features

Privacy first

Everything is processed locally in your browser, never uploaded.

Instant

Results as you type.

Completely free

Free to use, no limits.

FAQ

What is a token?
A token is the basic unit LLMs process — roughly 0.75 English words or 0.5-1 Chinese characters. Models bill per token, so understanding it helps control cost.
How do I estimate AI call cost?
Count tokens, then multiply: input tokens × input rate + output tokens × output rate for a single-call estimate.
How accurate is it?
Approximate tokenization — within ~10% of GPT/Claude counts — sufficient for budgeting and length control.
Is online counting safe?
Safe. Computation runs locally; text never uploads, suitable for unpublished prompts and code.
What is the token counter best for?
Prompt length control, API cost estimation, context-limit avoidance, dataset sizing and model pricing comparison.
Are there limits on free use?
No. Free, no registration, real-time long-text counting.
What does 128K context mean?
The model handles ~131072 tokens per conversation; the progress bar shows current usage to flag overruns.
Do Chinese and English differ?
Yes — English ~0.75 word/token, Chinese ~1 char/token; mixed text is estimated per segment.