LLM Toolkit Token ↔ Words Converter
1,000 tokens ≈ 750 words in English — that’s the headline. Enter any amount below for a precise conversion, or scan the reference table for common sizes.
Result
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English prose at 0.75 words per token. Other languages vary — see FAQ below.
Token to words reference table
| Tokens | ≈ Words | ≈ Characters | ≈ Pages | Fits in |
|---|---|---|---|---|
| 100 | 75 | 400 | 0.1 | every current model |
| 250 | 188 | 1,000 | 0.4 | every current model |
| 500 | 375 | 2,000 | 0.8 | every current model |
| 1,000 | 750 | 4,000 | 1.5 | every current model |
| 2,000 | 1,500 | 8,000 | 3.0 | every current model |
| 4,000 | 3,000 | 16,000 | 6.0 | every current model |
| 8,000 | 6,000 | 32,000 | 12 | every current model |
| 16,000 | 12,000 | 64,000 | 24 | every current model |
| 32,000 | 24,000 | 128,000 | 48 | every current model |
| 64,000 | 48,000 | 256,000 | 96 | every current model |
| 128,000 | 96,000 | 512,000 | 192 | every current model |
| 200,000 | 150,000 | 800,000 | 300 | all but the GPT-4o family (200K+) |
| 400,000 | 300,000 | 1,600,000 | 600 | GPT-5 / Claude 5 / Gemini (400K+) |
| 1,000,000 | 750,000 | 4,000,000 | 1500 | Gemini / Claude 5 only (1M) |
Estimates assume English prose: 0.75 words and 4 characters per token, 500 words per single-spaced page. The "fits in" column shows the smallest current model window that holds this many tokens with room for a response.
Frequently asked questions
How many words is 1,000 tokens?
Approximately 750 words of English text. English averages 1.33 tokens per word (0.75 words per token), so 1,000 tokens ≈ 750 words ≈ 4,000 characters ≈ 1.5 pages. The ratio holds well for prose but not for code, which tokenizes denser.
How many words is 500 tokens?
About 375 words of English text (500 × 0.75 words per token) — roughly half a single-spaced page, or 2,000 characters. In reverse, 500 words is about 665 tokens. The 500-token range is a common unit for per-request rate limits and small prompt templates.
How many words is 5,000 tokens?
About 3,750 words — a long-form article or roughly 7.5 single-spaced pages. That is around 20,000 characters of English prose. Code, tables and JSON tokenize denser than prose, so technical content converts to fewer words per token in practice.
How many words is 8,000 tokens?
About 6,000 words — roughly 12 single-spaced pages or 24,000 characters of English prose. 8K is the classic small-context tier that fits every model on the market today, which is why it is a common default for local and embedded inference where memory is tight.
How many words is 32,000 tokens?
About 24,000 words — roughly 48 single-spaced pages, or a short non-fiction book. 32K is a common context or output limit tier, and it fits comfortably inside every current model, including small open-weight models that run on a single consumer GPU.
How many words is 1 million tokens?
About 750,000 words — roughly 1,500 single-spaced pages, or seven to eight full-length novels. 1M tokens is the long-context tier of Gemini and Claude 5. Because you pay per token, a full 1M-token request is exactly 1,000× the cost of a 1,000-token request on any model — measure your real counts with our Token Counter before committing to long-context jobs.
How many tokens is 500 words?
About 665 tokens. At 1.33 tokens per English word, multiply your word count by 1.33 to estimate tokens. A typical blog post of 1,500 words runs ≈ 2,000 tokens; a 60,000-word manuscript runs ≈ 80,000 tokens — which fits comfortably in every current model’s context window.
How many tokens is a page of text?
A single-spaced page of English (≈500 words) is roughly 665 tokens. Double-spaced academic pages (≈250 words) run ≈330 tokens. So a 30-page document is about 20,000 tokens — small enough that most models can hold several documents at once.
Do different models have different words-per-token ratios?
Slightly, yes. Modern tokenizers (GPT-5’s o200k, Claude’s, Gemini’s) are all BPE-family and converge near 0.75 words per token for English. The big differences appear in other languages: Spanish and French run 1.5–2 tokens per word, German slightly higher, and CJK languages can exceed one token per character — meaning 1,000 tokens buys you far less Chinese text than English.
How many characters per token?
About 4 characters per token for English, including the space after each word. Common words ("the", "and", "is") are single tokens; rarer words split into 2–4 tokens. Numbers and unusual Unicode sequences tokenize less efficiently.
How many words is 1 token?
About 0.75 words — three-quarters of an English word. Flip it around and you get 1.33 tokens per word, the standard planning ratio for English prose. A 1-token chunk is typically a common word ("the", "and") or a word fragment, so short texts with many rare words convert to more tokens per word than long runs of common vocabulary.
Why does this matter for API costs?
Because you pay per token, not per word. Converting your content budget to tokens tells you your real API spend: 100 articles of 1,500 words each ≈ 200,000 tokens ≈ $0.05 to process on GPT-5 mini, $0.25 on GPT-5 and $0.01 on GPT-5 nano. Measure your actual text with our Token Counter for exact numbers.
Measure, don’t estimate
Ratios get you close; the AI Token Counter gets you exact — it runs OpenAI’s real tokenizer in your browser. Then price the result with the API Cost Calculator.