What is AI token efficiency?
AI token efficiency is the practice of reducing the tokens an AI coding assistant consumes per useful result. On GitHub Copilot and Claude Code, most of an agent session's spend goes on re-reading the session at every step, so the biggest savings come from keeping sessions short and the prompt cache warm, filtering what tools return, routing each task to the right model, and moving deterministic work out of the model loop.
Why the bill keeps growing
A token is roughly three-quarters of a word. In an agent session, one request becomes dozens of steps, and at every step the agent re-reads the whole session so far, so input dominates the bill. Long sessions also make the answers worse, so every usable result costs more. The same practices fix both.
What the guide shows your team
- Cut what agents re-read. Filter test and log output to failures, move fixed steps out of the model loop, and start a fresh session when one drifts.
- Keep the prompt cache warm. What breaks the cache, and how to keep it working across a session.
- Route each task to the right model. Sonnet by default, Opus only when a task needs the reasoning, Haiku for subagents that read files.
- Cap every unattended run. Set a turn, budget or time limit before any pipeline run, and track cost per merged pull request.
- Read the 2026 bills correctly. How Copilot's shared credit pool works, and what the current Claude prices mean for your default model.
Certance estimates that teams applying these practices together typically cut monthly token spend by 30 to 50%, without losing quality. Every price in the guide is checked against vendor documentation, and every figure is sourced or labelled as an estimate.
