Microsoft integrates Chatgpt-5 into its ecosystem

Shortly after Openaai presented its new GPT-5 models, Microsoft already announces the integration into Microsoft 365 Copilot, Copilot, Azure Ai Foundry, Github Copilot and other applications. According to the group, the upgrades are now available.

Chatgpt-5 for copilot

According to its own information, Microsoft does not rely on a single Chatgpt 5 model; A so -called “Model Router” decides a control level operating in the background, which is to be used by the new models. Users do not have to deal with the question of which is the right model for the respective task.

According to Microsoft, the smart mode in the Copilot should enable the AI assistant to switch between models so that users can use the right AI model for their purpose.

Interested parties can try chatt-5 with Microsoft Copilot for free at Copilot.microsoft.com or via the copilot app on Windows, Mac, Android and iOS devices, explains the company.

Openais new model for companies and developers

Microsoft-365 copilot users also get access to GPT-5: The corporate copilot should be able to think better, keep an overview in longer conversations and better understand the context of the user, explains Microsoft in an announcement article.

In addition, the new AI model should help to open up content from emails and documents to help users “keep their work tasks under control and be more productive”. In addition, GPT-5 also moves into the Copilot Studio platform for creating custom AI agents.

Developers also have access to Chatgpt-5: The group is now making the new model available for all paid Github copilot tariffs so that you can try the AI when writing code. With the Azure-Ai Foundry extension, developers in VS-Code should also use GPT-5 to develop agents-without having to leave the code editor, writes Microsoft.

In addition, the Group GPT-5 also provides the Azure-Ai Foundry. This enables developers to use the model in AI-based applications: Developers can use the model router in the Azure Ai Foundry, “to ensure that the correct model is used for the respective task or query”.