GitHub Copilot is evolving rapidly, with new features and models released regularly. If you're an enterprise administrator, staying informed helps you make confident decisions about which capabilities to enable, when to adopt them, and how to manage risk across your organizations.
This article provides guidance on navigating the Copilot landscape, understanding different types of features and models, and preparing for upcoming changes.
Keeping up to date with Copilot changes
You can use GitHub Docs and the GitHub Blog to keep track of new releases.
Learning about new Copilot features
To learn about new Copilot features, we recommend monitoring these two locations:
- Features overview: For a complete list of Copilot capabilities, see GitHub Copilot features.
- Changelog: Follow the Copilot changelog for announcements about new and updated features.
Copilot features generally fall into three categories:
Each feature has its own enablement requirements and policy settings. When a new feature is released:
- Review the feature documentation to understand its capabilities.
- Check the policy settings available at the enterprise and organization level. See GitHub Copilot policies to control availability of features and models.
- Consider running a pilot with a subset of users before broader rollout.
Learning about new Copilot models
GitHub Copilot uses multiple AI models from different providers to power its features. Different models have different strengths: some prioritize speed and cost-efficiency, while others are optimized for accuracy, reasoning, or working with multimodal inputs (like images and code together).
Users will have access to different models depending on the feature they're using and your enterprise's Copilot plan and policies.
You can find information about the models available and upcoming models in the following locations:
- Supported models: For a complete list of available models and their capabilities, see Supported AI models in GitHub Copilot.
- Model comparison: To compare model capabilities side by side, see AI model comparison.
- Changelog: Model updates are announced in the Copilot changelog.
To plan for model transitions and set user expectations, track which models GitHub designates as base or long-term support (LTS). Copilot automatically falls back to a base model when premium requests run out:
| Model type | Description | Why it matters |
|---|---|---|
| Base model | The default model when no other models are enabled. | Automatically enabled within 60 days of designation. |
| LTS model | A model supported for one year from designation. | Allows enterprises to build workflows around a stable model. |
| Fallback | When premium requests are exhausted, Copilot uses an earlier base model. | Ensures continuous access at no additional cost. |
For more information, see Base and long-term support (LTS) models.
Considering different release stages
Copilot features and models progress through different release stages. Understanding release stages helps you decide when to enable features for your organization. Each stage has different expectations for stability and support.
Tip
Many preview features are covered by the GitHub Data Protection Agreement, meaning you can test them without breaching compliance requirements. See GitHub DPA-Covered Previews for a list.
Preview features are controlled by policy settings at the organization and enterprise level. The Opt in to preview features policy allows administrators to enable or disable preview features on the GitHub website specifically. See Managing policies and features for GitHub Copilot in your enterprise and Managing policies and features for GitHub Copilot in your organization.
Reviewing information about Responsible AI
GitHub is committed to responsible AI development and provides transparency documentation for each Copilot feature.
Each Copilot feature has a dedicated responsible use article describing its purpose, capabilities, and limitations. See Responsible use of GitHub Copilot features.