GitHub Copilot gets GPT-5.5, but the 7.5x multiplier changes the math

Original: GPT-5.5 is generally available for GitHub Copilot View original →

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LLM Apr 27, 2026 By Insights AI 2 min read 1 views Source

GitHub has started rolling GPT-5.5 into Copilot, which turns OpenAI's latest model from a release-note item into an option developers can actually choose inside daily tools. In its April 24 changelog post, GitHub said early testing showed GPT-5.5 performing best on complex, multi-step agentic coding tasks and solving some real-world problems earlier GPT models could not.

The distribution footprint is the real headline. GitHub says GPT-5.5 is appearing in the model picker for Visual Studio Code, Visual Studio, Copilot CLI, the Copilot cloud agent, github.com, GitHub Mobile on iOS and Android, JetBrains, Xcode, and Eclipse. That is broader than a narrow preview inside one editor, and it suggests GitHub wants the same model choice to travel across IDE, terminal, browser, and mobile review workflows.

Access is not universal. GitHub says the rollout targets Copilot Pro+, Copilot Business, and Copilot Enterprise, and it is happening gradually. For Business and Enterprise customers, administrators must explicitly enable the GPT-5.5 policy in Copilot settings before teams can use it. That makes this both a model launch and a governance event: the model may exist in the product, but org admins still control whether it lands on developer desktops.

There is also a cost signal attached. GitHub says GPT-5.5 launches with a 7.5x premium request multiplier under promotional pricing. That single number changes the adoption conversation, especially for teams already comparing model quality against seat cost, usage caps, and internal approval rules. A stronger coding model inside Copilot matters, but so does the bill that follows it.

The near-term question is whether developers decide the extra capability is worth that multiplier. GitHub has already widened the places where GPT-5.5 can show up; now the real test is whether the model becomes a default pick for hard coding tasks or stays reserved for the expensive edge cases.

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