A review of OpenAI o1 and how we evaluate coding agents

OpenAI's new o1 models, specifically o1-mini and o1-preview, have been evaluated using an AI software engineering agent named Devin. This evaluation focuses on how these models enhance coding tasks, particularly in reasoning and problem-solving. Devin utilizes a combination of model inferences to effectively plan and execute coding tasks. The o1-preview model demonstrated superior performance compared to the previous GPT-4o model, particularly in its ability to analyze problems, backtrack, and arrive at correct solutions without hallucinating. The evaluation methodology involved realistic coding tasks that mimic real-world scenarios, allowing for autonomous feedback and learning. The internal benchmark, cognition-golden, showed significant performance improvements when key subsystems were switched from GPT-4o to the o1 series. The evaluation process also included simulated user interactions and evaluator agents that autonomously assessed Devin's outputs. The findings suggest that the o1 models enhance the reliability and effectiveness of coding agents, making them more adept at handling complex software engineering tasks. The ultimate goal is to create a safe, steerable, and reliable coding agent that can be confidently deployed in production environments.

- OpenAI's o1 models improve coding agents' reasoning and problem-solving capabilities.

- Devin, the AI agent, showed significant performance gains using o1-preview over GPT-4o.

- The evaluation methodology included realistic tasks and autonomous feedback mechanisms.

- The cognition-golden benchmark demonstrated measurable improvements in coding tasks.

- The focus is on developing reliable and steerable coding agents for production use.