What is a Token (AI)? - Definition & Meaning
Learn what tokens are in the context of AI and language models, how tokenization works, and why tokens matter for the costs and performance of LLMs.
Definition
A token is the basic unit by which AI language models process text. Text is split into tokens — these can be whole words, parts of words, or punctuation marks. Tokens determine both the processing capacity (context window) and the cost of using a language model.
Technical explanation
Tokenization is the process of converting text into a sequence of numerical tokens the model can process. Modern LLMs use subword tokenization algorithms like Byte-Pair Encoding (BPE), WordPiece, or SentencePiece. BPE starts with individual characters and iteratively merges the most common pairs into tokens. In English, one token averages 3-4 characters or 0.75 words; in Dutch, the ratio is similar but slightly less efficient due to compound words. A model's context window (e.g., 128K tokens for GPT-4) determines how much text the model can process simultaneously, including the prompt, system instruction, RAG context, and generated response. Costs for cloud LLMs are calculated per token, with separate rates for input tokens and output tokens. Tokenizers are model-specific: text that costs 100 tokens in GPT-4 may yield a different number of tokens in Claude or LLaMA.
How OpenClaw Installeren applies this
OpenClaw Installeren optimizes token usage for your AI assistant through efficient prompt templates, intelligent RAG chunking, and context window management. Our configuration minimizes unnecessary tokens in every API call, directly resulting in lower costs for cloud-based LLMs and faster response times for locally hosted models.
Practical examples
- The sentence "OpenClaw Installeren is a deployment platform" gets split by GPT-4 into approximately 8 tokens: ["Open", "Cl", "aw", " Install", "eren", " is", " a", " deployment", " platform"].
- A customer service chatbot processing 100,000 messages per month consumes roughly 50 million tokens with GPT-4o-mini, costing just a few dozen euros in API fees.
- A 10-page technical document contains an average of 3,000-4,000 tokens, fitting comfortably within the context window of modern LLMs.
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