How many tokens is a word?

Roughly 0.75 words per token or 1.33 tokens per word for English text. Varies by text type - code and technical text often use more tokens. Use tokenizer tools to check.

Why do some words use more tokens?

Tokenizers learn common patterns. Frequent words get single tokens; rare words split into subwords. "Indistinguishable" might be 3-4 tokens while "the" is 1.

Do all LLMs use the same tokens?

No. Different models use different tokenizers. Text that's 100 tokens in GPT-4 might be 110 tokens in Claude. Don't assume tokenization transfers between models.

How can I see how my text tokenizes?

Use tools like OpenAI's Tokenizer (platform.openai.com/tokenizer), Hugging Face tokenizer visualization, or programming libraries like tiktoken (Python).

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Tokenization

The process of breaking text into smaller units (tokens) that AI models can process. Tokens might be words, subwords, or characters depending on the tokenizer.

In-Depth Explanation

Tokenization converts raw text into the numerical tokens that language models actually process. Understanding tokenization helps explain model behaviour and costs.

Tokenization approaches:

Word-level: Each word is a token (limited vocabulary)
Character-level: Each character is a token (long sequences)
Subword: Balance of both (BPE, WordPiece, SentencePiece)
Byte-level: Operating on raw bytes (GPT-2/3/4 style)

Key tokenizers:

BPE: GPT models, learns merge rules from data
WordPiece: BERT, similar to BPE
SentencePiece: Language-agnostic, used by many models
Tiktoken: OpenAI's fast BPE implementation

Tokenization effects:

Token count affects API costs (priced per token)
Context window limits are in tokens, not words
Rare words split into multiple tokens
Different languages tokenize differently

Business Context

Understanding tokenization helps estimate API costs, work within context limits, and debug unexpected model behaviour with unusual text.

How Clever Ops Uses This

We help Australian businesses understand tokenization for cost estimation and optimisation when deploying LLM solutions.

Example Use Case

"Estimating costs: a 1000-word document is roughly 1300 tokens with GPT-4, so processing 10,000 documents would cost approximately $X at current API rates."

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