Cost formula
Tokens ≈ words × tokens-per-word; Words ≈ tokens ÷ tokens-per-word; English planning default ≈ 1.33 tokens/word (≈ 0.75 words/token); Cost = calculateApiCost(input tokens, output tokens, requests)
Calculator
Words to tokens calculator
Turn word counts into token estimates (or reverse), adjust for English, code, or multilingual text, and see what that volume costs on major LLM APIs.
Words ↔ tokens
1,330 tokens
$4.40/ month at this volume
1,000 words · ~4.0 pages · GPT-4o mini
Input tokens1,330
Output tokens400
Tokens1,330
Tokens / word1.33
Cost / request$0.00044
Monthly cost$4.40
Inputs
Convert words and tokens
Plan from word counts before you have a finished prompt—then price the API.
Direction
Content profile
OpenAI rule of thumb: ~750 words ≈ 1,000 tokens.
Quantity
API pricing
Insights
Planning reference
Estimates only—paste real prompts in the token counter for BPE accuracy.
Words1,000
Tokens (est.)1,330
Words / token0.75
≈ Pages (250 w/page)4.0
Cost / request$0.00044
Need an exact OpenAI-style count from pasted text? Use the token counter with BPE tokenization, then bring those numbers back here for volume pricing.
Word↔token ratios are planning heuristics (not billing-grade tokenization). Exact counts vary by model tokenizer, language, and punctuation. Updated 2026-07-31. Approximate static list prices for planning only. Always verify against each provider’s official pricing page before production budgeting.
Guide
How to get value from this calculator
Convert words to tokens (and tokens back to words) with content-type ratios for English prose, technical docs, code, or multilingual text—then estimate per-request and monthly API cost. Use this when you know document length in words but do not yet have a final prompt to tokenize.
Why it matters
“How many tokens in 1,000 words?” is one of the most common LLM planning questions. A fast words↔tokens converter plus cost turns blog length, docs, and scripts into budget numbers before engineering builds the prompt.
How to use it
- Choose Words → tokens or Tokens → words.
- Pick a content profile (English, technical, code, or multilingual) or set a custom tokens/word ratio.
- Enter your word count (or token count) and optional word presets.
- Set expected output tokens and monthly request volume.
- Select a model to price the estimated tokens.
- Read estimated tokens/words, pages, cost per request, and monthly spend.
- For billing-grade counts from pasted text, open the token counter next.
Planning tips
- English prose ≈ 750 words per 1,000 tokens; code and non-English are denser.
- A double-spaced page is often ~250 words—use the page estimate as a sanity check.
- If you already have the final prompt, prefer the BPE token counter over ratios.
- System prompts that ship on every request should be converted and multiplied by volume.
- Custom ratios beat defaults once you measure real tokenizer output for your corpus.
- Output tokens are separate—short classifications cost far less than long generations.
Frequently asked questions
How many tokens are in 1,000 words?
For typical English prose, about 1,300–1,400 tokens (≈ 1.33 tokens per word). Technical writing, code, and other languages are usually higher. This calculator lets you pick a profile or custom ratio.
Is this the same as the token counter?
No. The token counter pastes text and estimates with tokenizer-style methods. Words to tokens starts from word or token counts for early planning, then prices API volume.
Are word-to-token ratios exact for billing?
No. They are planning heuristics. Exact billing uses each model’s tokenizer. Confirm with provider tools or the token counter when accuracy matters.
How many words is 1,000 tokens?
About 750 English words at the common 0.75 words/token rule. Switch to Tokens → words mode to convert with your chosen ratio.