What is Token Counting?
Token counting measures how many tokens are in a prompt or response. Learn why token counts matter for OpenClaw API costs and context windows.
Definition
Token Counting
Token counting is the process of measuring how many tokens a piece of text contains when processed by a language model's tokenizer. Tokens are the fundamental units that LLMs process — they can be words, subwords, or individual characters depending on the tokenizer.
Why It Matters
Why You Should Care
Token counts directly determine API costs (you pay per input and output token) and whether your prompt fits within a model's context window. For OpenClaw users, understanding token counts helps estimate monthly spend, avoid context window overflows, and identify prompts that are strong candidates for compression.
How It Works
Under the Hood
Each LLM uses a tokenizer that splits text into tokens. For example, the word "compression" might be split into "compress" and "ion" as two tokens. Most English words are 1-3 tokens. Spaces, punctuation, and code can add additional tokens. Tools and APIs provide token counting utilities to measure text before sending it.
Related Terms
Keep Learning
Prompt Compression
Prompt compression reduces the number of tokens in AI prompts while preserving meaning. Learn how it works and why it matters for OpenClaw API costs.
Context Window
A context window is the maximum number of tokens an LLM can process. Learn about context limits and how claw.zip compression extends them for OpenClaw users.
LLM API Costs
LLM APIs charge per token for input and output. Learn how pricing works, what drives OpenClaw costs, and how to reduce AI API spend by 80-93%.
Token Optimization
Token optimization reduces the number of tokens consumed by AI API calls. Learn techniques for minimizing token usage and OpenClaw costs.
See Token Counting in Action
Try claw.zip free and experience the difference for yourself.