
Share
Microsoft is overhauling GitHub Copilot's pricing model to token-based billing and tightening rate limits, signaling a shift towards more accurate cost reflection as AI compute expenses escalate.
Microsoft has announced significant changes to its GitHub Copilot AI coding assistant, including a shift to token-based billing and tighter rate limits. These adjustments come as the company aims to better align user costs with the actual compute expenses of running advanced AI models.
The shift to token-based billing is a strategic move by Microsoft to manage the growing costs associated with running GitHub Copilot. According to internal documents reviewed by Where’s Your Ed At, the weekly cost of operating the service has nearly doubled since January 2026. This trend mirrors the broader challenges faced by AI companies, which have been subsidizing compute costs to keep user fees low.
Token-based billing will ensure that users pay for the actual computational resources they consume, rather than a fixed number of requests. For instance, more complex models like Claude Opus 4.7 currently cost $5 per million input tokens and $25 per million output tokens. This pricing model is designed to be fairer and more sustainable, as it directly correlates with the compute usage.

At present, GitHub Copilot users are allocated a certain number of "requests" based on their subscription tier:
More expensive models use more requests, while cheaper ones use fewer. For example, a complex model might consume multiple requests for a single interaction, whereas a simpler model might require only one request.
The shift to token-based billing will see users charged based on the number of tokens their prompts and outputs generate. This change is expected to be more transparent and fair, as it directly reflects the computational resources consumed. However, the exact timing for this transition has not been specified by Microsoft.
Tags
Original Sources
About the author
Marcus began tracking AI's market implications in 2016, noticing AI-related patent filings accelerating ahead of earnings upgrades before most of the sell-side had caught on. A former fixed-income quantitative analyst, he spent two decades building models that priced risk across emerging markets before pivoting to cover the economic impact of AI full-time. His writing translates opaque technical developments into clear risk/reward terms — and he's rarely diplomatic about the gap between AI valuations and underlying fundamentals. He believes most market participants still underestimate AI's long-run deflationary effect on knowledge work.
More from The Analyst →This Week's Edition
21 April 2026
88 articles
Related Articles
Related Articles
More Stories