Token Management Cheat Sheet

Token management is the discipline of controlling, optimizing, and tracking how Large Language Models (LLMs) consume input and output tokens—the fundamental units text is broken into for processing. With tokens driving both API costs (often 1–30 per million output tokens in 2026) and context window limits (up to 2M tokens), effective token management determines whether AI applications run efficiently or exhaust budgets. Key insight: output tokens cost 3–5× more than input tokens, reasoning models generate thousands of hidden thinking tokens billed at output rates, agentic workflows burn 10–100× more tokens than simple API calls, and even tool schema definitions can silently consume 55K–134K tokens before any work begins—making optimization a production necessity at every layer of your stack.