# How many tokens is my text? AI token counter, context windows and API cost explained

> A token is about four English characters or three quarters of a word. A 10,000-word document is roughly 14,000 tokens and costs about 4 cents to read at $3 per million. Why Hindi and other scripts use more tokens, what a context window really limits, and how to estimate a prompt before you send it.

Published 2026-10-23 by Toolcloak. 4 minute read. Original: https://toolcloak.com/articles/how-many-tokens-is-my-text-ai-token-counter-context-windows-and-api-cost-explained
Related free tool: [AI Token Counter](https://toolcloak.com/tools/token-counter)

AI models do not read words. They read **tokens**, small pieces of text that may be a whole short word, part of a long one, a space or a punctuation mark. Tokens decide three things you care about: how much text fits in one request, how long the answer can be, and what you pay if you use a model through an API. Knowing roughly how many tokens your text holds saves you from cut-off documents and surprise bills.

> **In short**
> - **Rule of thumb for English:** 1 token ≈ 4 characters ≈ ¾ of a word. 1,000 words is about 1,400 tokens.
> - **Other scripts cost more.** Hindi, Tamil and other non-Latin text uses noticeably more tokens for the same meaning.
> - **The context window is the total a model can handle at once,** your prompt plus its reply.
> - **Cost = tokens ÷ 1,000,000 × price per million.** 14,000 tokens at $3 per million is about $0.04.

## What a token is

**How text becomes tokens**

1. **Your text.** “Explain how photosynthesis works in simple words.”
2. **Split into pieces.** Common words stay whole. Rare words break into parts, like photo + syn + thesis.
3. **Each piece gets a number.** The model works on these numbers, not on letters.
4. **Count the pieces.** That count is your token count for the prompt.

*The exact split depends on the model’s tokenizer. Common English words are usually one token, and rare or long words take several.*

Try it: [AI Token Counter](https://toolcloak.com/tools/token-counter). Paste text, pick a model family and a price, and get an estimated token count, word count, cost and share of a context window.

The tool estimates tokens from character counts, using about 4 characters per token for OpenAI and Gemini families, 3.8 for Claude and 3.6 for Llama. It is an estimate, not a real tokenizer. For billing-level accuracy, use the token figures your provider reports.

## Words, characters and tokens compared

| Text | Characters | Words | Tokens (about) |
| --- | --- | --- | --- |
| A sentence like “Explain how photosynthesis works in simple words for a 10 year old.” | 67 | 12 | 17 |
| A short email | 600 | 100 | 150 |
| One page of text | 3,000 | 500 | 750 |
| A 10,000-word report | 57,000 | 10,000 | 14,300 |
| A 300-page book | about 500,000 | about 90,000 | about 125,000 |

*At about 4 characters per token. Real counts vary with vocabulary, formatting and language.*

## Why Hindi and other scripts use more tokens

Tokenizers were built mostly on English text, so common English words are compact and other scripts are chopped into smaller pieces. In the tool’s estimate, every non-English-letter character counts as about 0.9 tokens, against 0.25 for an English letter. A short Hindi phrase of 32 characters is about 29 tokens, while an English sentence twice as long is only 17.

**Same idea, different token cost (tool’s estimate)**

| English sentence | Hindi phrase |
| --- | --- |
| ≈ 17 tokens | ≈ 29 tokens |
| 67 characters | 32 characters, about half the length |
| About 4 characters per token | Almost every character costs close to a token |
| Most words are single tokens | Real tokenizers vary, but the gap is real |

*If you work in Hindi or another Indian language, expect your text to use more of the context window and cost more per idea.*

- **Plan for more tokens** when a prompt or document is not in English.
- **Newer tokenizers are getting better** at non-English text, so the gap changes with the model.
- **Measure with your own text,** not an English sample.

## Context window: the limit that counts both ways

A model has a **context window**, the most tokens it can hold at once. It includes your instructions, any pasted document, the conversation so far and the answer it is writing. If you fill the window with input, there is less room for the reply. The tool shows what share of a typical window your text uses, with example sizes of 128,000 tokens for OpenAI and Llama families, 200,000 for Claude and 1,000,000 for Gemini. Actual windows differ by model and version, so check the one you use.

**Typical context windows used by the tool (thousand tokens)**

|  | Tokens |
| --- | --- |
| OpenAI / Llama | 128K |
| Claude | 200K |
| Gemini | 1,000K |

*128K tokens is roughly 90,000 English words, or a 300-page book. Hindi text fills it faster.*

> **Leave room for the answer:** If you paste a long document and ask for a long summary, count both. A prompt that fills 95 percent of the window leaves very little for the reply. For long files, split the text into chunks. The AI text chunker tool does this, and the context window calculator helps you plan.

## What it costs

> **Cost:** Cost = tokens ÷ 1,000,000 × price per million tokens. The tool starts with $3 per million input tokens as an example. Enter your model’s real price. Output tokens are usually priced higher than input tokens, so a long answer costs more than a long question.

**Input cost at $3 per million tokens**

|  | Value | Note |
| --- | --- | --- |
| 1,000 tokens | $0.003 | A long email |
| 14,300 tokens | $0.043 | A 10,000-word report |
| 100,000 tokens | $0.30 | A short book |
| 1 million tokens | $3.00 | Several books |

*Prices differ widely between models and change often. For a deeper look at running costs across many calls, see the [LLM cost guide](https://toolcloak.com/articles/ai-tokens-and-api-cost-why-the-bill-depends-on-output-and-chat-history).*

## Ways to use fewer tokens

- **Cut filler.** Remove greetings, repeated instructions and boilerplate from long prompts.
- **Trim documents** to the relevant part instead of pasting everything.
- **Strip extra whitespace and formatting.** Tidy text with the [word and character limits guide](https://toolcloak.com/articles/how-long-is-long-enough-a-field-guide-to-word-and-character-limits).
- **Ask for the length you need.** “Answer in 5 bullet points” stops a long reply.
- **Reuse a short system prompt** instead of repeating a long one each time.

The same habit of checking sizes before you send applies to files: the [data storage units guide](https://toolcloak.com/articles/why-a-1-tb-drive-shows-931-gb-kb-kib-mb-and-mbps-explained) explains bytes and bits, and the [readability guide](https://toolcloak.com/articles/flesch-reading-ease-what-your-readability-score-means) helps you make a prompt clear. If you do not care about exact counts and just want to know how long a text is, the [word counter guide](https://toolcloak.com/articles/how-long-is-long-enough-a-field-guide-to-word-and-character-limits) is quicker.

## Frequently asked questions

### What is a token in AI?

A token is a small piece of text that a language model reads and writes: a short word, part of a longer word, a space or punctuation. In English, one token is about four characters or three quarters of a word.

### How many tokens is 1,000 words?

About 1,300 to 1,500 tokens for typical English text. Technical text, code and non-English languages use more.

### AI token kya hota hai?

Token text ka chhota tukda hota hai jo AI model padhta hai, jaise ek chhota shabd ya shabd ka hissa. English me lagbhag 4 characters ya aadhe se zyada shabd (3/4 word) ek token hota hai. Hindi me same baat ke liye zyada token lagte hain.

### What is a context window?

It is the maximum number of tokens a model can handle at once, counting your prompt, any documents and its reply. If the input fills it, there is less room for the answer.

### Why does Hindi use more tokens than English?

Tokenizers were built mostly from English text, so English words compress well while other scripts are split into many smaller pieces. The exact gap depends on the model.

### How do I calculate the cost of a prompt?

Divide the number of tokens by one million and multiply by your model’s price per million tokens. Output tokens are usually priced separately and are often more expensive.

### Is this token counter exact?

No. It estimates from character counts. Each model has its own tokenizer, so exact numbers differ. Use your provider’s reported counts for billing.

### Is my text sent anywhere when I count tokens?

No. The counting is done in your browser, and the text is not uploaded.