What GitHub Changed: From Requests to Tokens
GitHub Copilot’s new token-based billing model replaces fixed subscriptions and request quotas with AI Credits that meter usage by tokens consumed across prompts, responses, and cached context, causing many developers to see sharply higher AI usage costs and forcing them to track consumption far more closely than before. Under the legacy Copilot subscription model, users paid a flat fee and drew from pools of standard and premium requests, even when running long autonomous coding sessions or complex refactors. GitHub has now retired that system and tied Copilot pricing to token-based billing, where every model call burns through a credit balance that maps directly to the size of the interaction and the model’s cost tier. This shift ends the cross-subsidy in which GitHub absorbed escalating inference expenses and exposes how expensive long chats, large context windows, and frontier models can be for everyday workflows.
How Token-Based Billing and AI Credits Work Now
In the new GitHub Copilot pricing, each paid plan includes a bundle of GitHub AI Credits, and one credit represents one cent of usage. The Pro tier at USD 10 (approx. RM46) per month includes 1,500 credits, Pro+ at USD 39 (approx. RM180) includes 7,000 credits, and the new Copilot Max plan at USD 100 (approx. RM460) includes 20,000 credits. Plan prices are unchanged, but the structure is different: base credits are matched 1:1 to the subscription price, while a flex allotment adds extra credits that GitHub can adjust as model economics change. Credits are consumed on all token-based interactions, including input, output, and cached data, at published per-model rates. Lighter models can deliver one million output tokens for around the cost of a single credit digit, whereas frontier models can cost orders of magnitude more for the same volume, so model choice now heavily shapes AI usage costs.
Real-World Impact: Bills Jumping 10x and Credits Vanishing
Developers report that the Copilot subscription changes have translated into sudden, steep cost increases and much faster credit burn. Some users moved from roughly USD 39 (approx. RM180) in monthly spend to projected bills near USD 1,800 (approx. RM8,280) once their usage was priced by tokens instead of requests. Others describe watching months’ worth of AI Credits disappear in a single day of normal work, particularly when using advanced models like Claude Sonnet 4.6 or frontier OpenAI models. One commenter noted that updating only a few lines across six files cost about USD 0.35 (approx. RM1.60) per line, while another saw their balance drop from 7,000 credits to 3,705 after one day. According to TechSpot, the new metered billing “is forcing many developers to confront something they had largely ignored: how many tokens their everyday coding habits consume and what that usage actually costs.”
New Plans, Budget Controls, and Lost Credits
To soften the move to token-based billing, GitHub introduced the USD 100 (approx. RM460) Copilot Max plan, which includes USD 200 (approx. RM920) in monthly AI Credits, aimed at heavy users who run long agentic sessions. Business and Enterprise plans keep per-seat prices of USD 19 (approx. RM87) and USD 39 (approx. RM180) with matching credit allotments and added promotional credits through August, plus organization-level controls to monitor and cap AI usage costs. However, developers who had accumulated unused credits under the old premium request system saw those wiped when metered billing went live, meaning carefully banked allowances vanished overnight. While these new caps, estimation tools, and model multipliers give teams more control, they also require constant attention to credit balances and model choices. The net result is a more transparent but far stricter system that aligns Copilot subscription changes with the true cost of large-scale AI.
Practical Tips to Keep Copilot Costs Under Control
For developers staying on Copilot, controlling AI usage costs now means designing workflows around the meter. The safest starting point is to favor smaller, cheaper models for everyday tasks and reserve frontier models for high-value problems, since one million tokens on a nano-scale model can cost a tiny fraction of the same output from GPT-5.5. Keep prompts short and specific, avoid sprawling multi-hour agent sessions, and break work into focused questions instead of continuous chat. Use GitHub’s estimation tools to compare past usage against new token-based billing and set budgets that match your plan’s AI Credits. Teams on Business and Enterprise plans should enforce organization-wide defaults for model selection and context limits, then watch credit dashboards weekly. With the end of subsidized subscriptions, cost awareness has become a core skill: every long conversation and large diff now carries a visible price tag on your Copilot bill.
