Understanding Tokens in AI and LLMs

When you see storage measured in '2M tokens' or '20M tokens' on a pricing page, what does that actually mean? If you've ever wondered why AI companies charge based on tokens rather than characters or words, you're not alone. Understanding tokens is essential for anyone working with AI language models—especially when managing costs and capacity.

A token is the basic unit that AI language models use to process text. Think of it as a building block of language. When you send text to an AI model like GPT-4, Claude, or any other large language model (LLM), the system doesn't read your text word-by-word or letter-by-letter. Instead, it breaks everything down into tokens first.

Here's a simple example: The sentence 'Hello, how are you?' might be broken into tokens like this: ['Hello', ',', ' how', ' are', ' you', '?']. Notice that punctuation marks are separate tokens, and spaces often attach to the following word. A single word can be one token, or it can be split into multiple tokens—especially for longer or less common words.

In English, one token roughly equals 4 characters or about 0.75 words on average. So if you have 100 tokens, that's approximately 75 words or 400 characters. However, this ratio varies significantly across languages. Turkish, Arabic, and other non-Latin script languages often require more tokens per word because the AI models were primarily trained on English text.

Why do AI systems use tokens instead of counting words or characters? The answer lies in how these models work internally. Language models process information through neural networks that work with fixed-size inputs. Tokens provide a standardized way to represent any text—whether it's English, code, mathematical symbols, or emojis—in a format the model can understand.

Tokenization happens through a process called Byte Pair Encoding (BPE) or similar algorithms. The model learns the most common patterns in text during training and creates a vocabulary of tokens. Common words like 'the' or 'and' become single tokens. Rare or complex words get broken into smaller pieces. For example, 'unhappiness' might become ['un', 'happiness'] or ['un', 'happy', 'ness'] depending on the tokenizer.

This is why storage is measured in tokens rather than documents or pages. When Mycellia indexes your company documents, emails, and files, the text is converted into tokens for AI processing. A 10-page document might contain 3,000-5,000 words, which translates to roughly 4,000-7,000 tokens. However, a technical document with code snippets, special terminology, or non-English content could use significantly more tokens.

Understanding token counts matters for three reasons: cost, capacity, and performance. First, most AI services charge per token processed. If your pricing tier includes 2M tokens of storage, that's your indexed knowledge base capacity—approximately 1.5 million words or 2,500-4,000 pages of typical business documents. Second, every query you send to the AI uses tokens from your allowance. A simple question might use 50 tokens, while a complex analysis request with context could use 500+ tokens. Third, models have maximum context windows measured in tokens—typically 4K, 8K, 32K, or even 128K tokens—which determines how much text they can consider at once.

When you see storage tiers like '2M tokens' for Starter plans and '20M tokens' for Business plans, you can now estimate capacity. 2M tokens = approximately 1.5M words = 2,500-4,000 pages. 20M tokens = approximately 15M words = 25,000-40,000 pages. These are rough estimates because token counts depend on content type, language, and formatting.

For companies using Work AI platforms like Mycellia, token-based pricing aligns directly with resource usage. Storing and searching through more documents requires more processing power. The AI must convert queries to tokens, search the indexed token database, and generate responses—all measured in tokens. This makes token-based pricing more transparent and predictable than vague 'document limits' or 'user seats.'

One practical tip: if you want to estimate tokens before uploading documents, use this rule of thumb: 1 token ≈ 4 characters ≈ 0.75 words in English. For code, mathematical formulas, or technical content, assume 1 token ≈ 3 characters. For Turkish or other agglutinative languages, assume 1 token ≈ 2-3 characters. These estimates help you predict whether your content will fit within your storage tier.

Finally, remember that tokens are consumed in two ways: storage (your indexed knowledge base) and queries (questions you ask the AI). A 2M token storage tier means you can index up to 2M tokens of content. Separately, your monthly query allowance (like 500 queries/user/month) determines how many questions you can ask. Each query uses tokens based on the question length plus the retrieved context plus the AI's response.

In summary, tokens are the universal currency of AI language models—a standardized unit that works across all languages and content types. Understanding tokens helps you choose the right pricing tier, estimate capacity needs, and optimize your AI usage. Whether you're a developer integrating AI APIs or a business leader evaluating platforms, knowing that 2M tokens ≈ 2,500-4,000 pages gives you a practical benchmark for planning and budgeting.