⚡ Bidirectional Token & Word Converter
Type in either field to convert instantaneously in real-time.
📝 Live Text & Prompt Tokenizer
Paste any text, prompt, or article to calculate real-time word and token distribution.
💰 Multi-Model LLM API Cost Matrix
Compare exact cost to process your current token volume across leading 2026 AI models.
| Model | Input Price / 1M | Output Price / 1M | Est. Input Cost | Est. Output Cost |
|---|
Quick Token-to-Word Conversion Reference
Standard benchmark equivalents for English prose (1 word ≈ 1.33 tokens):
| Tokens | Estimated Words | Estimated Characters | Book Pages | Context Window Scope |
|---|---|---|---|---|
| 100 Tokens | ~75 Words | ~400 Chars | 0.3 Pages | Short tweet or single sentence |
| 500 Tokens | ~375 Words | ~2,000 Chars | 1.5 Pages | Single email or short blog post |
| 1,000 Tokens | ~750 Words | ~4,000 Chars | 3.0 Pages | Medium article or blog essay |
| 5,000 Tokens | ~3,750 Words | ~20,000 Chars | 15.0 Pages | Academic research paper or chapter |
| 10,000 Tokens | ~7,500 Words | ~40,000 Chars | 30.0 Pages | Comprehensive company report or guide |
| 32,000 Tokens | ~24,000 Words | ~128,000 Chars | 96.0 Pages | Standard small book or documentation |
| 128,000 Tokens | ~96,000 Words | ~512,000 Chars | 384.0 Pages | Full length 400-page novel (GPT-4o / Claude / DeepSeek) |
| 1,000,000 Tokens | ~750,000 Words | ~4,000,000 Chars | 3,000.0 Pages | Encyclopedia or complete codebase (Gemini 1.5 Pro) |
Tokenization & LLM Sizing Guide
What is a token in AI language models?
A token is the basic unit of text that a Large Language Model (LLM) processes. Instead of reading whole words or individual letters, models use algorithms like Byte-Pair Encoding (BPE) to group common characters into sub-word tokens. In English, common words like "the" are 1 token, while complex or rare words might be split into 2 or 3 tokens.
How many tokens is a typical book?
A standard 80,000-word novel translates to approximately 106,000 tokens in English prose. This easily fits within modern 128k context windows offered by DeepSeek-V3, GPT-4o, and Claude 3.5 Sonnet.
Why is DeepSeek so much cheaper per token than OpenAI?
DeepSeek leverages a Mixture-of-Experts (MoE) architecture with Multi-Head Latent Attention (MLA) and aggressive FP8 training. This radically cuts inference computation costs, allowing DeepSeek to price input tokens at $0.27 per 1M tokens compared to OpenAI's $2.50 per 1M tokens.