First Look: Exploring OpenAI o1 in GitHub Copilot
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Today OpenAI released OpenAI o1, a new series of AI models equipped with advanced reasoning capabilities to solve difficult problems. Like you, we are excited to put the new o1 model through its paces and have tested the integration of o1-preview with GitHub Copilot. While we explore many use cases with this new model, such as debugging large systems, refactoring legacy code, and writing test suites, our initial testing showed promising results in code analysis and optimization. This is due to o1-preview’s ability to think through challenges before acting on them, allowing Copilot to break down complex tasks into structured steps.
In this blog we describe two scenarios that show the capabilities of the new model in Copilot and how it could work for your everyday life. Read on for insight into what happens when a new model comes to market, what we test, and how we approach AI-powered software development at GitHub.
Optimize complex algorithms with advanced reasoning
In our first test, we wanted to understand how o1-preview can help write or refine complex algorithms – a task that requires deep logical thinking to find more efficient or innovative solutions. Developers need to understand the limitations, optimize edge cases, and iteratively improve the algorithm without losing sight of the overall goal. This is exactly where o1-preview stands out. With this in mind, we developed a new code optimization workflow that takes advantage of the model’s reasoning capabilities.
In this demo a new integrated Optimize The chat command instantly provides rich editor context such as imports, tests, etc Performance profiles. We tested how well o1-preview can analyze and iterate code to achieve more thorough and efficient optimization in one fell swoop.
The video shows how to optimize the performance of a Byte pair encoder Used in Copilot chats Tokenizer library (Yes, that means we use AI to optimize an important building block of AI development.)
This was a real problem for the VS Code team because Copilot has to repeatedly tokenize large amounts of data when composing prompts.
The results illustrate how o1-preview’s reasoning ability enables a deeper understanding of the code’s limitations and edge cases, helping to achieve a more efficient and higher quality result. Meanwhile, GPT-4o sticks with obvious optimizations and would need a developer’s help to steer Copilot towards more complex approaches.
Beyond tackling complex code tasks, o1-preview’s mathematical capabilities shine as it effortlessly calculates benchmark results from raw terminal output and then summarizes them succinctly.
Optimize the application code to resolve a performance issue
In this next demo on GitHub, o1-preview was able to identify and develop a solution for this a performance error within minutes. It took a few hours for one of our software engineers to find the same solution to the same error. At the time, we wanted to add a folder tree to the file view in GitHub.com, but the number of items caused our focus management code to stall and crash the browser. The video shows side by side the difference between using GPT-4o and o1-preview to resolve the issue:
Since this code manages 1,000 items, it was difficult to isolate the problem. Finally, we implemented a change that improved the runtime of this function from over 1,000 ms to around 16 ms. If we had had Copilot with o1-preview, we would have been able to identify the problem quickly and fix it more quickly.
Through these experiments, we noticed a subtle but effective difference: o1-preview’s conscious and targeted responses make it easy for the developer to pinpoint problems and implement solutions quickly. With GPT-4o, a similar prompt could result in a blob of code instead of a solution with line-by-line recommendations.
We bring the power of o1-preview to developers building on GitHub
Not only are we excited to experiment with integrating o1-preview with GitHub Copilot, but we can’t wait to see what you can build with it. That’s why we’re launching the o1 series GitHub Models. You’ll find o1-preview and o1-mini, a smaller, faster and 80% cheaper model, on our marketplace later today, but since it’s still in preview, you’ll have to do it Sign up for Azure AI for early access.
Stay tuned
As part of Microsoft’s collaboration with OpenAI, GitHub is able to continually explore how we can leverage the latest AI breakthroughs to increase developer productivity and, most importantly, increase developer satisfaction. Although these demos demonstrate the advanced capabilities of o1-preview for two specific optimization problems, we are still early in our experiments and are excited to see what else it can do.
We are currently exploring additional use cases for Copilot – in IDEs, Copilot workspaceand on GitHub – to take advantage of o1-preview’s powerful argumentation features and accelerate developers’ workflows even further. The advances we’re sharing today barely scratch the surface of what developers can create with o1-preview in GitHub Copilot. And given the expected evolution of both the o1 and GPT series, this is just the beginning.
Are you interested in trying out the latest Copilot and AI innovations?
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