GitHub Copilot AI Credits Explained: Plan Limits and Cost Controls

What changed

Since June 1, 2026, GitHub Copilot’s AI-heavy features use GitHub AI Credits. GitHub converts token costs for input, output, and cached context into credits at a fixed rate of one credit per $0.01. Code completions and Next Edit suggestions remain included, while chat, agent work, and other model interactions draw from the monthly allowance. Copilot code review can also consume GitHub Actions minutes in addition to AI credits. These rules come from GitHub’s announcement and current billing documentation.

That makes the subscription price only one part of the decision. The other part is how much metered model work your normal projects consume. GitHub does not bill “one prompt” at one fixed amount: model choice and token volume determine the cost. A short question to a lightweight model can use far fewer credits than a long agent session that reads a large repository.

Individual plans compared

The prices and allowances are from GitHub’s individual-plan table, checked July 29, 2026. The use-case column is TechNest’s guidance, not a vendor performance claim. GitHub describes base credits as tied to the subscription and flex credits as an additional allowance. Recheck the live table before buying because the total includes that separately defined flex portion.

The chart uses a zero-to-25,000 scale, so Max reaches 80% rather than being normalized into a full bar. It compares allowance size only; it does not claim that a higher tier is faster, more accurate, or better value.

Why prompts-per-month estimates fail

Copilot meters the tokens sent to a model, generated in response, and reused or stored as cached context. The selected model’s published token rates determine how many credits an interaction consumes. Repository size, conversation history, requested output length, and agent duration can therefore change the cost even when two users send the same number of requests.

Independent coverage from Tom’s Hardware corroborates the three current plan totals and notes that long conversations and model choice make usage hard to predict. Its examples of unusually large cost increases are user reports, not a representative benchmark, so they should be treated as a warning about variability rather than a forecast.

Two exceptions matter. First, real-time completions and Next Edit suggestions do not consume AI credits, according to GitHub. A developer who mainly uses inline suggestions may get more mileage from Pro than someone running repository-wide agents. Second, code review has a separate Actions component. A review can draw AI credits and GitHub Actions minutes, so the credit allowance alone cannot describe its full possible cost.

What happens when credits run out

An individual user can wait for the monthly reset, upgrade, or configure an additional-usage budget. Extra usage follows the same conversion: a $10 budget corresponds to 1,000 credits. GitHub says additional usage may be capped based on account and billing factors, so setting a budget is not a guarantee of unlimited capacity.

If you upgrade during a billing cycle, GitHub charges the price difference and makes the higher plan’s total allowance available after subtracting credits already used that month. That can be useful when the pattern repeats. It is a poor reason to upgrade permanently after one exceptional migration or unusually long agent task.

Use this conservative routine:

1. Begin with no overage budget, or a small amount you accept as a hard variable cost.
2. Run representative, non-sensitive work.
3. Open Settings → Billing and licensing → AI usage.
4. Compare month-to-date consumption with the working days remaining.
5. Upgrade or raise the budget only when repeated work justifies it.

GitHub’s monitoring guide says individual subscribers can see included credits used, additional usage, a chart by model, and model-level cost. TechNest did not access or test a subscriber account; this workflow is based on current documentation.

Which plan fits

Start with Pro when completions are the main benefit and metered chat or agents are occasional. The $10 fixed fee is the lowest paid commitment, and the dashboard can show whether the 1,500-credit total fits a normal cycle.

Move to Pro+ when repeated real work exceeds Pro’s allowance or premium-model access is a requirement. The price rises from $10 to $39 and the current total rises from 1,500 to 7,000 credits. Do not choose it from that ratio alone: confirm that the needed models and workflows are available and that the saved work is worth the difference.

Choose Max only with evidence of sustained high-volume use. Its 20,000 credits are the largest individual allowance, but its $100 price is ten times Pro’s. Max purchases more capacity and priority access described by GitHub; it does not guarantee better output.

The useful metric is cost per completed, reviewed task, not credits per dollar alone. Track several ordinary jobs and record consumption plus whether the output survives review. Do not upload credentials, private customer data, or material your organization has not approved for an AI service.

Transition details

Existing annual Pro and Pro+ subscribers can remain on legacy request-based billing until their terms expire unless they convert earlier. Independent reporting from ITPro corroborated that transition rule and the June 1 change. Confirm the billing model before comparing your allowance with a colleague’s account.

Availability also changed during rollout. GitHub paused individual sign-ups, then announced a gradual reopening on June 17. Check the live selector rather than an old screenshot.

Bottom line

For most individuals, Pro is the evidence-gathering baseline. Pro+ fits recurring use that demonstrably exceeds Pro or needs premium models. Max is a capacity purchase for sustained use. Whichever tier you choose, keep the subscription price, included-credit total, and overage budget separate; monitor usage by model; and leave overage at zero or a deliberately small amount until your normal workload is clear.