Token (LLM)
Tokens are the basic units AI models process - roughly 3/4 of a word. Understanding tokens helps you predict costs and optimize AI agent performance.
A token is the basic unit of text that large language models process - roughly 3/4 of a word in English. Token count determines AI processing costs, response limits, and context window usage.
Text Segmentation
Models split text into tokens - common words are single tokens, rare words split into multiple tokens. "Automation" is one token; "assistants.ae" might be three.
Cost Basis
AI API pricing is per token. Input tokens (your prompt) and output tokens (the response) are billed separately, often at different rates.
Context Window
Models have a maximum token limit (context window). GPT-4 supports 128K tokens. This determines how much text the model can process at once.
Language Variation
Arabic text uses more tokens per word than English. A 100-word Arabic paragraph might use 150-200 tokens vs. 130-140 tokens for English.
Cost Optimization
Efficient prompts use fewer tokens. Clear, concise instructions cost less than verbose prompts while often producing better results.
Speed Impact
More tokens means longer processing time. Shorter responses are faster and cheaper - relevant for real-time customer interactions.
FAQ
How much does a token cost?
Depends on the model. GPT-4o costs roughly $2.50 per million input tokens and $10 per million output tokens. A typical customer support interaction uses 500-2,000 tokens total.
Why does Arabic cost more than English?
Arabic tokenization is less efficient - Arabic words often split into more tokens than English equivalents. This is a known limitation of current tokenizers, not a pricing decision.
Can I reduce token usage?
Yes. Concise system prompts, efficient response formats, and caching common queries all reduce token consumption. OpenClaw optimizes these during setup.
Understand AI costs
Learn how tokens affect your AI agent pricing.