Term
Input Token
Tokens you send to an AI model in an API call — your prompt, the context, attached documents. Billed separately from output tokens and usually much cheaper.
Input token — in more detail
Input tokens are everything you hand the model on a call: system prompt, user question, attached context (documents, code, conversation history), tool definitions. Before the model can respond, it has to read these tokens and convert them into its internal representations. That step is what is billed as input tokens — typically between $0.15 and $3 per million tokens, depending on model and provider.
Example / practical context
A typical coding request with 30,000 tokens of repository context plus a short user question consists almost entirely of input tokens. With Claude Sonnet ($3/MTok input), that single request already costs $0.09 in input — multiplied by hundreds of requests per day, it becomes the dominant cost line. Whoever reduces input tokens (smaller contexts, targeted chunking, prompt caching) saves immediately.
Distinction from related concepts
Output tokens are what the model sends back — and are usually three to five times more expensive than input tokens. Cached input tokens are billed at a steep discount when the same prompt prefix is sent repeatedly (often just 10 % of the regular input price). The sum of input and output tokens must stay within the model’s context window.
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Glossary$/MTok (Cost per Million Tokens)
Standard pricing unit for AI APIs — cost in US dollars per one million processed tokens. Listed separately for input, output and sometimes cache.
EncyclopediaFunction Calling / Tool Use
How an LLM uses tools: define a tool as a schema, the model picks the function and arguments, the result returns to the chat — the basis of every agent.