# Token (LLM)

> A token is the unit LLMs read and write — a word fragment of roughly 3–4 characters in English. Models are priced, limited, and measured in tokens, not words.

**A token is the basic unit a language model reads and writes — typically a word fragment averaging 3–4 characters of English text. Everything about LLMs is denominated in tokens: pricing, context limits, and speed.**

Models don't see letters or words; a *tokenizer* splits text into pieces from a fixed vocabulary, and the model predicts one token at a time. "Understanding" is a single token; "unfathomable" might be three. The practical conversions: ~100 tokens ≈ 75 English words; code and non-English text usually run denser.

Tokens matter because they're the meter on everything. API pricing is per million input and output tokens (output costing several times more — generation is sequential, reading is parallel). The [context window](/glossary/context-window) is a token budget. Throughput is tokens per second. So the everyday engineering moves — trimming prompts, [caching repeated prefixes](/glossary/prompt-caching), summarizing history — are all token economics; the full playbook is in [LLM Cost and Latency Engineering](/guides/advanced/llm-cost-latency-engineering).

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_Source: https://agentscamp.com/glossary/llm-token — Term on AgentsCamp._