What GitHub Copilot’s Token-Based Billing Actually Means
GitHub Copilot’s new token-based billing model charges developers for the volume of AI tokens their sessions consume, rather than a fixed subscription that hides usage differences behind a single monthly price. Under the old system, Copilot mixed a flat fee with a pool of premium requests, but those requests were not tightly linked to how many tokens the models spent on each task. Now, Copilot bills through GitHub AI Credits, which deplete as models process input, produce output, and use cached data. According to The New Stack, plan prices stay the same, but every tier includes a set amount of AI Credits that maps to an underlying token budget. This shift reflects how Copilot has grown from simple autocompletion into agent-style sessions that can run across large codebases for extended periods.
How AI Credits, Flex Allotments, and Copilot Max Work
GitHub Copilot pricing now centers on AI Credits, with each paid plan including a fixed base allotment plus, for individual tiers, a variable flex allotment. Pro users paying USD 10 (approx. RM46) per month receive USD 15 (approx. RM69) in credits, while Pro+ users paying USD 39 (approx. RM179) per month receive USD 70 (approx. RM321) in credits. The flex portion is designed to change over time as model pricing and infrastructure costs evolve, so the included usage can adjust without rewriting every plan. For heavy users, GitHub introduced Copilot Max at USD 100 (approx. RM460) per month, which includes USD 200 (approx. RM920) in credits and is aimed at sustained, high-volume agentic work. Business and Enterprise seats keep per-user prices but receive credits equal to the per-seat fee, without any flex top-up.
From Per-Request to Per-Token: Why Some Bills Jump
The biggest shock for many developers is that Copilot now charges for how much the AI writes, not how many times you call it. Under the old premium request model, a single request could trigger heavy computation without directly increasing your bill, because GitHub was absorbing much of the cost. Under token-based billing, long chats, broad refactors, and multi-file agents burn through tokens fast. PCMag reports that some users saw more than half their monthly credits disappear in a single day, and one user who previously paid USD 39 (approx. RM179) per month estimated a new monthly cost approaching USD 1,800 (approx. RM8,280). That gap comes from workflows built around frequent, verbose interactions, which scale poorly under per-token charges tied to AI output volume.
Estimating Your GitHub Copilot Costs Before You Switch
To estimate your GitHub Copilot pricing under the new system, start by measuring how you use it today. Look at how often you rely on long-form chat, repository-wide agents, or code review, because these generate more AI output and therefore more tokens. GitHub provides an estimation tool that compares historic usage with token-based billing, which can highlight whether you fall into a low-, medium-, or high-consumption pattern. For individuals, compare your current subscription tier with its included credits and ask how many long sessions you run in a typical week. For organizations, remember that credits are pooled at the org level, so a few power users can consume most of the budget. Translate those patterns into a rough monthly token footprint, then see how that fits within the included AI credits for your plan.
Practical Ways to Control Token Consumption and Avoid Bill Shock
Staying on top of per-token charges means changing how you work with Copilot. First, move routine autocompletion and short answers to features that do not consume credits, such as basic code completions included in all paid plans. Reserve long agent sessions and repository-wide refactors for problems that genuinely need them, and keep prompts focused instead of open-ended so the AI outputs fewer, more relevant tokens. For teams, use the new budget controls: set conservative user-level budgets so one engineer cannot drain the pool, then add enterprise-wide limits to cap metered overage. Watch daily or weekly usage dashboards and lower limits if you see credits disappearing too fast. Many users quoted by PCMag say they are shifting to a “very focused” style of AI usage; that mindset is key to keeping costs predictable.