Getting more from each token: How Copilot improves context handling and model routing

figure — as Copilot takes on more agentic work, from planning and editing to debugging, reviewing, and calling tools across longer sessions, efficiency means more than using fewer tokens. It means being smarter about how you use them.

Increasing efficiency starts with reducing what Copilot has to repeat from turn to turn, including context, tool definitions, and cached state. It continues with choosing the right model for the job. A quick explanation, a focused edit, and a complex multi-file change should not all be treated the same way. We are working on both: improving the Copilot harness so more of each session goes toward the task itself, and expanding Auto so Copilot can pick the model that fits the work without asking developers to make that choice every time. This post focuses on harness improvements in GitHub Copilot for VS Code and on ongoing work to expand Auto across Copilot surfaces. Increased prompt caching and deferred tools In longer GitHub Copilot sessions in VS Code, the harness prepares a lot of recurring information for the model: instructions, repository context, conversation history, available tools, and the current state of the task. Some of that context is needed. Some of it can be cached, deferred, or loaded only when it becomes relevant. Two improvements in GitHub Copilot for VS Code are doing most of the work here. Prompt caching helps Copilot reuse model state for repeated prompt prefixes instead of recomputing the same prefix on every request. Tool search lets the model load tool definitions on demand, instead of sending every full tool schema into context on every turn. That matters more as agents use more tools. A session may need access to MCP tools, terminal commands, file operations, workspace search, and product-specific actions. Loading every full tool definition up front adds fixed cost to each turn, even when only a small number of tools are relevant to the task. With tool search, Copilot can keep the available toolset broad while sending less unnecessary tool schema into the model.