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About this tool
Convert an LLM token budget into an approximate word count, page count and reading time by content type.
This converter turns an LLM token budget into an approximate word count, page count and reading time, based on the documented rule of thumb that 1 token equals about three-quarters of an English word (4 characters). It helps writers, students and developers answer the practical question behind every context window and max-token setting: how much actual text does this budget hold? Presets adjust the ratio for technical writing, non-English languages and source code, which all tokenize more heavily than plain prose.
Open Tokens to Words Converter on AltFTool — it loads instantly in your browser.
Paste or upload your source content.
Choose the target format or conversion mode.
Copy or download the converted result.
Separate presets for prose, technical text, European languages and code, since each yields fewer words per token.
Converts to ~500-words single-spaced or ~250-words double-spaced pages, plus minutes at an average 238 wpm reading speed.
One-tap buttons for common budgets: 1,024, 4,096, 8,192, 32,768 and 128,000 tokens.
About 3,072 words of plain English text, using the rule of thumb that 1 token is roughly three-quarters of a word. For technical writing expect closer to 2,450 words, and for source code as little as 1,600, because both tokenize more heavily than everyday prose.
Roughly 96,000 English words, which is about 192 single-spaced pages (at ~500 words per page) or 384 double-spaced pages — approximately the length of a full novel. That is why 128k-context models can hold entire books or large codebases in a single prompt.
About 0.75 English words per token — equivalently, 1,000 tokens is roughly 750 words — per OpenAI's tokenizer documentation. The figure drops for other content: non-English European languages often land near 0.55 words per token and source code near 0.4, since punctuation and rare strings split into more tokens.
Input and output are counted separately by every major provider, and most price output tokens higher than input tokens. A model's context window limits input plus output together, while a max-tokens setting usually caps only the generated output — so convert the two budgets separately.
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