GitHub使Copilot融合在2026宇宙中一般可用

AI 模型和平台

GitHub使Copilot融合在2026宇宙中一般可用

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GitHub制造了Copilot Fusion，其多型号的路由和管弦系统，一般于2026年10月28日提供，与新的GitHub Copilotapp,Copilot项目并列于GitHub宇宙2026上揭幕，更深的Copilot代码审查分级。

这些公告来自位于Microsoft的CoreAI执行副总裁Jay Parikh的"University 2026"基调帖子，其团队包括GitHub，以及一系列同一天的更动日志条目中详细列出可用性，计费，预览术语。

Copilot 融合路线和管弦乐模型

在取型器中选择Copilot Fusion，根据Copilot Fusion changlog选择了通路和管弦系统而不是特定的模型。 对于每个快取，Fusion决定使用哪些模型以及它们如何合作，将工程HydraFusion的多模式管弦与Copilot的汽车模型选择特征背后的微调模型选择结合起来。

用户从三个工作级别中选择一个。 Fusion Standard优先处理日常编码的多功能模型，Fusion High针对挑战性的任务和更深层次的推理，Fusion Max为最艰巨的任务和复杂的推理保留了最能胜任的模型。 对于每个任务，Fusion运行三个执行工作流程中的一个：Single，一个模型直接解决了任务；Cascade，一个高效的模型起草一个响应，一个质量检查可以接受它或者升级为更强大的模型；Critic，一个模型起草，第二个模型审查，原始模型修改。

GitHub 称 Copilot Fusion Max在人工分析编码Agent指数上打出60分，在Claude Code与Opus 5.5 Max的两分内，每分任务平均API成本大约为一半，使用约一半的令牌，每分任务的时间减少约40%.

管理者通过企业和组织模式政策控制哪些模型可以被Fusion所借鉴，他们可以禁用模型用于Fusion，而不会令每个人失去功能。 企业管理设置也可以将Fusion设置在任何努力级别上，作为Copilot用户的默认模式。

Hari Pokhrel是AT&T公司的主要软件工程师，在"Fusion Standard"(Fusion Standard)的变换日志中，称通过为每项任务自动选择正确的模型来提高整个AT&T的符号效率，减少不必要的开支并保持高价值工作的能力，其决策也随着时间的推移而得到改进。

Fusion按每个型号的标准费率向每个型号开出账单，使用付费Copilot计划的用户在使用时会获得有限时间的30%的模型成本折扣。 该系统可在Visual Studio Code,GitHub Copilot App,GitHub Copilot CLI，和JetBrains IDEs的所有Copilot计划上使用，并有额外的客户端支持。 在研究预览期间启用了自動，HydraFusion，或HydraFusion Max的开发者会自动过渡到相应的Fusion努力级别。

GitHub Copilot App和Copilot项目

GitHub Copilot app是为代理软件的发送而设计的，Parikh写道，连接了agents和跨项目的工具，并支持代码以外的工作，包括写出规格，介绍设计讨论，以及在一个地表分析遥测。

Copilot项目，自2026年10月28日起在app中本土提供，让用户运行agent在全管理，平行会话中跨多个寄存器工作。 Copilot App changlog标签为Copilot Projects 一个公开预览：用户描述一个目标，附加诸如寄存器，设计模拟等上下文，或Slack线程，选择模型和工具，并接收拉出请求复核等结果。 项目自动化处理可重复的任务，例如每当功能为获得时都会创建宣传视频。

该应用还可以从Slack和Figma等相接工具上下文，可以在聊天中从自然语言生成并编辑图像。 一个计算机使用功能，在macOS和Windows的公开预览中，让Copilot读取app内容，点击控制，输入或编辑文本，并跨桌面应用程序导航，包括没有API，命令行界面，或MCP集成的应用程序。 Copilot在控制一个应用程序前请求批准，组织可以通过所管理的设置来禁用该特性。

会话连续性让用户在GitHub应用，VS代码，CLI，和网络上开始并跟随同名云 agent会话。

代码审查最大、 自动化和企业访问

GitHub也作推出 Copilot代码审查 Max，而Parikh 称在Review Bench上贴出最高的F1分，用于高分问题。 现有的 " 文学 " 和 " 平衡 " 级仍用于日常审查，" 马克斯 " 计划用于需要更深入研究的变革。

Copilot自动化在云中运行的存储器内部创建了AI-动力工作流程，该工作流程通过人工触发，由事件引发，或按时间表，用于发行分解和代码审查等任务。 GitHub 称自动化器现在可以学习其成果，并提议改进供用户审查和批准。

2026年10月28日起，GitHub Entertainment 客户可以使用所有Copilot特性，包括Copilot应用，代码审查，以及自动化，而无需单独购买订阅，代之以支付他们使用的AI信用。 可通过GitHub Sales立即提供这一变化，自服务器即将到来；现有的Copilot Business和Copilot Entertainment 客户可以保留其目前的计划，或待准备后搬走。

从海合一和基础设施增加的道路

Fusion的一般可用性遵循了工程Hydrafusion研究预览GitHub 推出于2026年9月4日，在当年早些时候的发布汽车模型选择之后。 在附带预览的离线评价中，固定的海合金政策将核实的任务质量提高了4.9个百分点，比Bench 2.1号终点站的Claude Opus 5的估计费用低了67%，在DeepSWE的1.5个百分点以36%的低成本达到1.5个百分点，在Bench检查站的0.1个百分点以65%的低成本达到0.1个百分点，GitHub的内部基准测试是用真正的Copilot会议建造的。 HydraFusion的三种执行模式——Single,Cascade，和Critique——被带入了Fusion的工作流程集。

GitHub 称它在2026年迄今为止增加了400万个CPU核心，160个PB高速存储，50个Tb/s网络容量。 3个原则指导了它新的Git架构：简化了原始体，将存储从计算中解开，并尽量减少了协调。 该公司正在迁移到Azure的多地区部署，预计于2027年初完成这一迁移。

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AI Models & Platforms

GitHub Makes Copilot Fusion Generally Available at Universe 2026

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GitHub made Copilot Fusion, its multi-model routing and orchestration system, generally available on October 28, 2026, unveiling it at GitHub Universe 2026 alongside the new GitHub Copilot app, Copilot projects, and a deeper Copilot code review tier.

The announcements arrived in a Universe 2026 keynote post from Jay Parikh, Executive Vice President of CoreAI at Microsoft, whose teams include GitHub, and in a series of same-day changelog entries detailing availability, billing, and preview terms.

How Copilot Fusion Routes and Orchestrates Models

Selecting Copilot Fusion in the model picker chooses a routing and orchestration system rather than a specific model, according to the Copilot Fusion changelog. For each prompt, Fusion determines which models to use and how they work together, combining the multi-model orchestration of Project HydraFusion with the fine-tuned model selection behind Copilot’s auto model selection feature.

Users pick one of three effort levels. Fusion Standard prioritizes versatile models for everyday coding, Fusion High targets challenging tasks and deeper reasoning, and Fusion Max reserves the most capable models for the most demanding tasks and complex reasoning. For each task, Fusion runs one of three execution workflows: Single, where one model solves the task directly; Cascade, where an efficient model drafts a response and a quality check accepts it or escalates to a stronger model; and Critique, where one model drafts, a second model reviews, and the original model revises.

GitHub said Copilot Fusion Max scored 60 on the Artificial Analysis Coding Agent Index, within two points of Claude Code with Opus 5.5 Max, at about half the average API cost per task, using about half as many tokens and about 40% less time per task.

Administrators control which models Fusion can draw on through enterprise and organization model policies, and they can disable models for Fusion without disabling them for everyone. Enterprise managed settings can also set Fusion, at any effort level, as the default model for Copilot users.

Hari Pokhrel, a principal software engineer at AT&T, said in the changelog that Fusion Standard improved token efficiency across AT&T by automatically selecting the right model for each task, reducing unnecessary spend and preserving capacity for higher-value work, and that its decision-making has improved over time.

Fusion is billed per model at each model’s standard rate, and users on paid Copilot plans receive a limited-time 30% discount on model costs when using it. The system is available on all Copilot plans in Visual Studio Code, the GitHub Copilot app, GitHub Copilot CLI, and JetBrains IDEs, with additional client support to come. Developers who enabled auto, HydraFusion, or HydraFusion Max during the research preview will transition automatically to the corresponding Fusion effort levels.

The GitHub Copilot App and Copilot Projects

The GitHub Copilot app is designed for agentic software delivery, Parikh wrote, connecting agents and tools across projects and supporting work beyond code, including writing specs, presenting design discussions, and analyzing telemetry in one surface.

Copilot projects, available natively in the app starting October 28, 2026, let users run agent work across multiple repositories in fully managed, parallel sessions. The Copilot app changelog labels Copilot Projects a public preview: users describe a goal, attach context such as repositories, design mocks, or Slack threads, choose models and tools, and receive results such as pull requests for review. Project automations handle repeatable tasks, such as creating a promo video whenever a feature lands.

The app can also use context from connected tools such as Slack and Figma, and it can generate and edit images from natural language in chat. A computer-use feature, in public preview on macOS and Windows, lets Copilot read app content, click controls, enter or edit text, and navigate across desktop apps, including those without an API, command-line interface, or MCP integration. Copilot asks for approval before controlling an app, and organizations can disable the feature through managed settings.

Session continuity lets users start and follow the same cloud agent session across the GitHub app, VS Code, the CLI, and the web.

Code Review Max, Automations, and Enterprise Access

GitHub also introduced Copilot code review Max, which Parikh said posted the highest F1 score on ReviewBench for high-severity issues. The existing Lite and Balanced tiers remain for everyday reviews, with Max intended for changes that need a deeper look.

Copilot automations create AI-powered workflows inside a repository that run in the cloud, triggered manually, by events, or on a schedule, for tasks such as issue triage and code review. GitHub said automations can now learn from their outcomes and propose improvements for users to review and approve.

Starting October 28, 2026, GitHub Enterprise customers can use all Copilot features, including the Copilot app, code review, and automations, without buying a separate subscription, paying instead for the AI credits they use. The change is available immediately through GitHub Sales with self-serve coming soon; existing Copilot Business and Copilot Enterprise customers can keep their current plans or move over when ready.

The Road From HydraFusion and Infrastructure Additions

Fusion’s general availability follows the Project HydraFusion research preview GitHub introduced on September 4, 2026, after launching auto model selection earlier in the year. In offline evaluations reported with the preview, fixed HydraFusion policies improved verified task quality by 4.9 percentage points at 67% lower estimated cost than Claude Opus 5 on TerminalBench 2.1, came within 1.5 percentage points at 36% lower cost on DeepSWE, and came within 0.1 percentage points at 65% lower cost on CheckpointBench, GitHub’s internal benchmark built from real Copilot sessions. HydraFusion’s three execution patterns — Single, Cascade, and Critique — carry into Fusion’s workflow set.

GitHub said it has added 4 million CPU cores, 160 PB of high-speed storage, and 50 Tb/s of network capacity so far in 2026. Three principles guide its new Git architecture: simplify the primitives, decouple storage from compute, and minimize coordination. The company is migrating to a multi-region deployment in Azure and expects to complete that migration in early 2027.

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