Words to Tokens Estimator
Estimate LLM token usage from a word count before you write, with content-type ratios and a safety margin.
About this tool
Words to Tokens Estimator
Estimate LLM token usage from a word count before you write, with content-type ratios and a safety margin.
The Words to Tokens 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.
How to use Words to Tokens Estimator
- 1
Open Words to Tokens Estimator on AltFTool — it loads instantly in your browser.
- 2
Type the size of the document into 'Planned word count' — it starts at 1000 — or click one of the 300, 500, 1,000, 2,000 and 5,000 word shortcut buttons underneath it.
- 3
Pick a ratio in the 'Content type' dropdown: Plain English prose at 1.33 tokens per word, Technical / jargon-heavy writing at 1.67, Non-English European language at 1.8 or Source code / JSON at 2.5, then set 'Safety margin (%)', which starts at 10.
- 4
Read the 'Estimated tokens' figure and the rows beneath it — Recommended budget, Approximate characters, Tokens per word used and Tokens per 1,000 words — then press 'Copy result'. Nothing needs calculating; the figures update as you type.
Why use Words to Tokens Estimator
Estimate before writing
Works from the planned word count alone — no text sample needed.
Content-aware ratios
Prose at ~1.33 tokens per word, technical text at ~1.67, European languages at ~1.8 and code at ~2.5.
Built-in safety margin
Adds a configurable buffer (default 10%) so the number you budget survives tokenizer variance.
Common use cases
- A developer sizing a max-tokens setting for a summarisation feature that ingests 2,000-word articles
- A content agency estimating monthly token consumption from its planned word output before picking an API pricing tier
- A prompt writer checking that a 5,000-word context document will fit under a model's input limit with room to spare
Frequently asked questions
How many tokens is 1,000 words?
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.
How many tokens is 500 words?
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.
Why do I need a safety margin on a token estimate?
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.
Do words in other languages use more tokens?
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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