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Learn how GitHub Copilot’s evolving models and infrastructure center developer choice and power agentic workflows.
Since its initial launch in 2021, GitHub Copilot has evolved a lot — and so have the AI models that power it.
When we first announced GitHub Copilot as a technical preview, OpenAI hadn’t yet launched ChatGPT. Today, AI dominates headlines and workflows alike. Amid this rapid change, our focus has remained the same: help developers stay in flow and get more done.
Amidst all of that, we have been focused on continually improving GitHub Copilot with developers in mind. That’s meant re-evaluating what models power it, and building agentic workflows into its core experience, too.
In this article, we’ll look at the models that drive different parts of GitHub Copilot and the powerful infrastructure that supports Copilot’s agentic capabilities. We’ll also discuss how model selection works across various features, like agent mode, code completions, and chat.
Now, let’s take a look under the hood. ✨
We’re constantly bringing new AI models to GitHub Copilot, and if you’ve been paying attention you know things change fast. This blog offers a point-in-time snapshot of the AI models we offer now, but bookmark our documentation to stay one step ahead.
When GitHub Copilot launched in 2021, it was powered by a single model: Codex, a descendant of GPT-3.
At the time, Codex was a revelation. Capable of understanding and generating code in the IDE with surprising fluency, Codex helped prove that AI could be a valuable tool for developers and showed a future where AI could potentially become a true coding companion.
Since then, Copilot has transitioned away from Codex and now defaults to the latest frontier models, while also giving developers access to their choice of advanced models.
Where it once lived firmly in the IDE as an extension to help developers with autocomplete and code generation, Copilot has evolved to become part of the GitHub platform available across developer workflows.
Copilot can answer questions, generate tests, debug code, get assigned an issue, generate a pull request, assist with code reviews, analyze codebases, and even fix security vulnerabilities, among other things.
Throughout all of these changes, we have focused on helping developers accomplish more, do less boilerplate work, stay in the flow, focus on the big picture, and ship higher-quality code faster.
Moving Copilot to a multi-model architecture wasn’t just about keeping up with AI advancements. It was about allowing developers to choose their preferred LLM for the task at hand, giving them flexibility in a rapidly changing environment.
Different models excel at different tasks, and by integrating a variety of them, GitHub Copilot can now deliver more tailored, powerful experiences through features like these:
Each option offers different trade-offs between speed, reasoning depth, and multimodal capabilities.
Because Copilot supports multiple models, developers have the autonomy to choose exactly how they build, whether they’re prioritizing speed, precision, or creativity. This flexibility lets developers tailor their experience based on their preferences — and these developer experience (DevEx) improvements translate into real productivity gains.
Copilot’s agentic capabilities mean that:
Agentic workflows help reduce complexity and prioritize developer choice at every step, leading to higher-quality code and fewer to-dos. This empowers developers to work the way they want: faster, safer, and with more confidence.
As AI continues to evolve, its role in shaping the developer experience will only grow. From reducing context switching to automating repetitive tasks, AI tools like Copilot are increasingly becoming a “second brain” for developers.
Having a choice of models lets developers customize exactly how they work. This lets them build with confidence, drive even more impact, and find greater satisfaction in their work.
GitHub Copilot is more than just one single AI model. It’s a dynamic platform that uses intelligence to match the right model with the right task. This flexibility is central to delivering a seamless DevEx, and it’s guided by a deep understanding of how developers work, what they need, and when they need it.
Development tasks vary in complexity and context. That’s why GitHub Copilot empowers users to select the model that best suits their needs, especially in Chat and agent mode.
Whether you’re optimizing for speed, reasoning depth, or multimodal input, there’s a model for you:
| Model | Best for: |
| o4-mini (OpenAI) | Speed, low-latency completions |
| GPT-4.1 (OpenAI) | Balanced performance and multimodal support |
| GPT-5 mini (OpenAI) | Lightweight reasoning |
| GPT-5 (OpenAI) | High-end reasoning for complex tasks |
| o3 (OpenAI) | Advanced planning and multi-step reasoning |
| Claude Sonnet 3.5 | Reliable, everyday coding tasks |
| Claude Sonnet 3.7 | Deeper reasoning for large codebases |
| Claude Sonnet 3.7 Thinking | Long-horizon, structured problem-solving |
| Claude Sonnet 4 | Higher reasoning depth |
| Claude Opus 4 | Premium reasoning power |
| Claude Opus 4.1 | Most advanced Anthropic option |
| Gemini 2.5 Pro | Advanced multimodal reasoning |
As the world of AI keeps evolving, so will the models that power GitHub Copilot. We’re committed to continuously refining and updating our AI infrastructure to provide you with the best possible developer experience.
We encourage you to explore all the different models available within Copilot and discover how they can enhance your coding journey. Happy building!
Interested in trying GitHub Copilot? Read the Docs to learn more about Copilot features or get started today.
