Cursor uses tokens as the fundamental unit of cost, context, and limits when interacting with AI models. Every AI interaction—autocomplete, chat, agent execution, or codebase query—consumes tokens from two sides: input tokens (your prompt, selected code, retrieved snippets, instructions) and output tokens (the generated response or edits). Cursor abstracts this from the user interface, but under the hood it is bound by the same constraints as any LLM-based system: finite context windows, per-request token caps, and plan-based usage limits.
In practical terms, token usage grows with context size and task complexity. Asking Cursor to refactor a single function uses relatively few tokens. Asking it to update multiple modules, reason across many files, or generate tests with extensive fixtures uses far more, because more code has to be retrieved and included in the prompt. This is why Cursor encourages scoped instructions and why large, unfocused requests can be slower, less reliable, or hit usage limits. Plans such as Pro or higher tiers typically raise token limits, allow larger context windows, or provide more agent executions per month, but they do not remove token economics entirely.
Understanding tokens helps you use Cursor more effectively. If you want reliable results, keep prompts tight, define scope clearly, and break large tasks into stages. This is the same discipline used in production AI systems. For example, if you are building a retrieval service that embeds documents and stores them in a vector database such as Milvus or Zilliz Cloud, you would not send the entire corpus to the model on every query. You retrieve only what matters. Cursor follows the same principle internally, and when you align your usage with that model—small, well-defined requests instead of giant “do everything” prompts—you get better results, lower token usage, and more predictable behavior.