The prompt token estimator counts approximate tokens in your text and estimates API costs across major LLM providers. Uses the ~4 characters per token rule for English text. Paste your prompt to see token count and cost breakdown.
Cost per 1M Requests (as input tokens)
Prices approximate, verify before productionHow Token Estimation Works
LLM APIs charge by token, not by character or word. Tokens are word-pieces — common words are one token, rare words split into multiple. For English text, approximately 4 characters = 1 token, or 1 word ≈ 1.3 tokens.
Token Count by Content Type
Plain English prose: ~4 chars/token. Code (Python, JS): ~3.5 chars/token (special characters tokenize individually). JSON: ~4-5 chars/token. Non-English languages: often 2-3 chars/token (many characters form single Unicode tokens). Numbers and punctuation: often 1 character per token.
FAQ
Is this token estimator free?
Yes, completely free with no signup. All estimation runs locally in your browser.
How accurate is the token estimate?
The ~4 characters per token rule is a useful approximation for English text with standard punctuation. Actual token counts depend on the specific tokenizer. GPT models use tiktoken (cl100k_base), Claude uses its own tokenizer. Code and non-English text often tokenize differently.
Why is my token count higher than expected?
Numbers, special characters, and punctuation often tokenize as separate tokens. Code is particularly token-dense — a single line with braces, dots, and operators can produce many tokens. Non-English text typically requires more tokens per word than English.
What is a token in the context of LLMs?
Tokens are the units LLMs process. They are roughly word-pieces — common words are single tokens, rare words split into multiple tokens. For GPT models, 1 token ≈ 4 characters of English text, or about 75% of a word. 1,000 tokens ≈ 750 words.
How do I reduce token usage?
Use shorter, more precise prompts. Remove redundant instructions. For RAG applications, optimize chunk size to include only relevant context. Use structured output formats that don't require verbose prose responses. Compress repetitive content.