GitHub Copilot Credit Billing: How Skills Help You Spend Less (2026)

On June 1, 2026, GitHub switched Copilot from flat-rate to usage-based billing with AI Credits. One credit equals $0.01. Premium request units are gone. Credits now get consumed based on token usage at per-model rates.

Basic completions and next-edit suggestions are still free and unlimited on paid plans. But agent mode, chat, and agentic features now burn credits. If you use Copilot for anything beyond autocomplete, your bill changed.

Quick Answer: GitHub Copilot now charges AI Credits based on token usage. Agent mode, chat, and multi-file edits consume credits. SKILL.md skills reduce credit burn by making the agent more efficient per task, fewer retries, less wasted context.

How the new credit system works

Your Copilot plan includes a base allocation of credits. When you exceed it, you pay overage or get throttled depending on your plan.

The credit cost varies by model. Claude Opus 4.5 through 4.8 ($5 in / $25 out per 1M tokens). Sonnet 4 through 4.6 ($3 / $15). GPT-5.5 ($5 / $30 at 272K context or less). GPT-5.4 ($2.50 / $15). Gemini 3.1 Pro ($2 / $12).

The key insight: output tokens cost 3-6x more than input tokens. An agent that generates verbose, unfocused output burns credits fast. An agent that generates precise, targeted output uses a fraction of the credits for the same result.

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What burns credits fastest

Agent mode. Copilot's agent reads files, runs terminal commands, and iterates. Each cycle consumes tokens on both input (reading your codebase) and output (generating changes). A complex multi-file refactor can burn through hundreds of thousands of tokens.

Model selection. Claude Opus 4.8 and GPT-5.5 cost 2-3x more per token than Sonnet or GPT-5.4. Picking the right model for the task matters more now than it did under flat-rate pricing.

Retries and iterations. When the agent gets it wrong and you ask it to try again, you're paying twice. Or three times. Every retry is new token spend.

How skills reduce credit burn

SKILL.md skills give the agent domain knowledge upfront. Instead of the agent exploring your codebase to figure out patterns, the skill tells it directly. This cuts input tokens (less exploration) and output tokens (more precise generation on the first pass).

Architecture skills prevent the agent from generating code that doesn't fit your patterns, which means fewer retries.

Testing skills with specific framework instructions generate correct tests the first time instead of guessing your setup.

Code review skills catch issues before you commit, preventing the downstream cost of fixing bugs with more agent cycles.

Token efficiency skills explicitly instruct the agent to minimize unnecessary output, reduce tool calls, and stay focused on the task.

The combined effect: 20-40% fewer tokens per task. On a credit-based system, that's 20-40% less spend.

Setting up skills in Copilot

Copilot supports SKILL.md in agent mode. Skills go in your repository:

your-project/.github/skills/skill-name/SKILL.md

Or use the /model slash command to select a model and skill combination that fits your budget for each task.

Browse credit-saving skills at agensi.io/skills.

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Related: GitHub Copilot Skills: SKILL.md Setup Guide and How to Reduce Claude Code Token Usage.