Copilot AI Credits 计算器

Plan allowances modeled here

The calculator uses the public monthly allowances documented by GitHub for Copilot usage-based billing. Individual plans have included AI Credits such as Pro, Pro+, and Max. Organization plans pool usage across assigned users. GitHub also documents promotional higher allowances for Business and Enterprise during the early migration window. The selector includes those promotional rows because the difference can be meaningful during the June-August 2026 transition period.

How to use this calculator

Start by separating ordinary completions from premium tasks. A completion in the editor is not the same as a large chat request, a coding-agent workflow, or an automated code review. Estimate only the work that draws from premium-model usage. If your organization has ten developers, do not simply multiply every chat by ten. Look at actual usage patterns: some users will stay in completions, while a few power users will run long agent loops and dominate the credit pool.

Then choose a model mix. Smaller models are usually cheaper and should handle routine edits, explanation, small bug fixes, and short code generation. Larger reasoning or Opus-class models are best reserved for hard pull-request reviews, architecture decisions, failing tests that require deep inspection, and long-running refactors. If you do not know the model mix yet, start with GPT-5.4 as the center case, then compare GPT-5.4 mini and a heavier model. The gap between those outputs is your routing opportunity.

The token fields should reflect real task context. A small question might use only a few thousand input tokens. A repository-aware task can include instructions, file snippets, logs, dependency output, and previous conversation. A large autonomous task can consume far more. Cached input can reduce cost when stable repository instructions, tool schemas, or repeated context are reused, but output tokens still matter. Long explanations, generated tests, and verbose review comments can be a hidden credit drain.

What to do if the estimate is high

First, reduce scope before you increase budget. Ask for a plan before implementation, point Copilot at specific files, paste only the failing error, and avoid asking one request to solve multiple unrelated problems. Second, route by difficulty. Use small models for easy tasks and reserve premium models for work that fails cheaper routes. Third, watch output size. A concise patch plus a short explanation is cheaper than a long narrative, especially across hundreds of monthly tasks.

For teams, add governance before enabling overage. Define who can use premium models, when Opus-class models are allowed, how pull-request review automation is triggered, and whether background tasks need approval. The best target is not zero credit usage. The target is predictable value per credit. A task that saves a senior engineer one hour may be worth a high-credit run. A task that summarizes obvious code comments probably is not.

FAQ

Does this replace GitHub Billing?

No. It is an independent estimate based on public documentation. GitHub Billing, Copilot settings, and exported usage are the source of truth.

Why do some Claude models include cache write cost?

GitHub's model pricing table includes cache write rates for some models. The calculator applies that rate to cached input volume for those rows so the estimate is closer to the public table.

What should I measure after migration?

Track credits by team, model, feature, and task class. Separate chat, agent, review, and background automation. The blended average hides the few workflows that usually drive spend.

Sources

• GitHub Blog: Copilot is moving to usage-based billing
• GitHub Docs: Copilot requests and AI Credits
• GitHub Docs: Copilot models and pricing
• Codex Credit Burn Calculator
• AI Coding Agent Cost Calculator

AI Code Limits is independent and is not affiliated with GitHub, Microsoft, Copilot, OpenAI, Anthropic, or Google.