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About this tool
Estimate LLM token usage from a word count before you write, with content-type ratios and a safety margin.
This estimator converts a planned word count into an LLM token estimate before any text exists, using the documented rule of thumb of about 4/3 tokens per English word (1 token ≈ 4 characters). It answers the budgeting question writers and developers hit constantly: how many tokens will my 1,000-word brief, prompt or document cost? Content-type presets raise the ratio for technical writing, non-English European languages and source code, and a configurable safety margin turns the estimate into a budget you can set limits against.
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Works from the planned word count alone — no text sample needed.
Prose at ~1.33 tokens per word, technical text at ~1.67, European languages at ~1.8 and code at ~2.5.
Adds a configurable buffer (default 10%) so the number you budget survives tokenizer variance.
About 1,334 tokens for plain English prose, using the documented ratio of roughly 4/3 tokens per word. Technical writing runs closer to 1,670 tokens, non-English European languages around 1,800, and source code as high as 2,500 tokens for the same nominal word count.
Roughly 667 tokens of plain English — the word count times 1.33. With this tool's default 10% safety margin, you would budget about 734 tokens to be safe against tokenizer variance.
Because character-based rules of thumb are typically only accurate to within 10–15% for English prose, and worse for unusual vocabulary. A 10% buffer is a common convention: it keeps a request from being truncated or rejected when the real tokenizer counts slightly higher than the estimate.
Yes. BPE tokenizers are trained mostly on English, so German, French or Spanish text typically costs 1.5 to 2 times more tokens per word, and non-Latin scripts such as Hindi or Japanese can cost several times more. If your workload is multilingual, estimate each language separately rather than applying the English ratio.
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