A GitHub Universe rendezvényen bejelentették a GitHub Copilot Fusion általános elérhetőségét, amely levételezi a fejlesztők válláról a modellek manuális kiválasztásának és összehangolásának terhét. A rendszer az egyes kódolási, hibakeresési vagy tervezési feladatok igényei alapján automatikusan dönt a legmegfelelőbb AI-modell használatáról.
A fejlesztőknek csupán az erőfeszítési szintet kell meghatározniuk: elérhető a Fusion Standard a mindennapi kódoláshoz, a Fusion High a nehezebb feladatokhoz, valamint a Fusion Max a legösszetettebb munkákhoz. A rendszer képes menet közben is váltani a modellek között, vagy több modellt egymással párhuzamosan ellenőriztetni. A GitHub adatai szerint a Fusion Max a benchmark teszteken a Claude Code teljesítményét nyújtotta közel feleannyi API-költséggel és 40 százalékkal gyorsabb válaszidővel.
A vállalati adminisztrátorok számára finomhangolási lehetőségek is rendelkezésre állnak: korlátozhatják, hogy a Fusion milyen modellekből válogathat, anélkül, hogy azokat teljesen letiltanák a csapat többi tagja elől. Az eszköz használatával a fejlesztőknek nem kell manuálisan váltogatniuk a drágább és az olcsóbb modellek között.
Az eredeti szöveg (The New Stack)
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GitHub has debuted GitHub Copilot Fusion, a new system that handles model selection and orchestration for coding tasks so developers don’t have to.
Combining the advanced runtime model orchestration from Project HydraFusion with Copilot Auto Model Selection, Copilot Fusion can now handle both model selection and orchestration. GitHub says Fusion Max delivered comparable benchmark performance to Claude Code with Opus 5.5 Max at about half the average API cost and in about 40% less time per task.
Generally available now, GitHub Copilot Fusion is part of GitHub’s new vision to make GitHub “the most comprehensive software delivery system in the world,” with developers delegating model choices while retaining responsibility for the direction and outcome of the work.
GitHub announced the news on Wednesday at GitHub Universe in San Francisco, the company’s flagship developer event.
For tasks involving reasoning, code generation, debugging, or tool use, GitHub says developers can delegate decisions about which models to use and how they work together. Its instructions: “choose Copilot Fusion and let Copilot intelligently route and orchestrate which models it uses for you.”
That sounds like a time-saver, since different tasks benefit from different models based on capabilities and cost. With Copilot Fusion working as the “model picker,” developers don’t have to weigh performance and price for every task.
More compelling is the idea that developers can use Copilot Fusion to strategically switch models mid-workflow, letting the system jump between models based on what best suits each step.
After analyzing what the task requires and choosing a model, the system orchestrates the next steps based on what makes the most sense. That may be a single model doing everything, a “cascading” workflow where a quality check evaluates one model’s response and then decides to accept it or escalate it, or a “critique” workflow where one model reviews another’s work.
That could reduce the mental load for developers who may inadvertently choose inefficient models when their attention drifts.
As Hari Pokhrel, principal software engineer at AT&T, describes in a statement for GitHub: “Previously, developers would often choose a more powerful model for a complex task and forget to switch back for everyday activities.”
While Copilot Fusion handles model selection and orchestration, developers choose the effort level: Fusion Standard for everyday coding; Fusion High for more challenging tasks and deeper reasoning; Fusion Max for the most demanding, complex work.
That’s if you’re free and comfortable letting GitHub’s systems take the lead. If you prefer, developers can implement other controls.
Per GitHub, enterprise administrators can control which models Copilot Fusion can choose from, disabling certain models for Copilot Fusion without disabling them for the entire developer team. Administrators can also adjust the policy state for each model independently and set whether Copilot Fusion is or isn’t the default model for Copilot users.
According to GitHub and Pokhrel’s statement, offloading model selection and orchestration to the new fusion system is the smarter route for developers, letting you balance performance and cost without the friction of choosing between models.
GitHub says it has improved performance since launching Project HydraFusion as a research preview in September 2026. On the Artificial Analysis Coding Agent Index, GitHub reports that Copilot Fusion Max scored 60, within two points of Claude Code with Opus 5.5 M
