Raising the bar on SWE-bench Verified with Claude 3.5 Sonnet

Our latest model, the upgraded Claude 3.5 Sonnet , achieved 49% on SWE-bench Verified, a software engineering evaluation, beating the previous state-of-the-art model's 45%. This post explains the "agent" we built around the model, and is intended to help developers get the best possible performance out of Claude 3.5 Sonnet.

SWE-bench is an AI evaluation benchmark that assesses a model's ability to complete real-world software engineering tasks. Specifically, it tests how the model can resolve GitHub issues from popular open-source Python repositories. For each task in the benchmark, the AI model is given a set up Python environment and the checkout (a local working copy) of the repository from just before the issue was resolved. The model then needs to understand, modify, and test the code before submitting its proposed solution.

Each solution is graded against the real unit tests from the pull request that closed the original GitHub issue. This tests whether the AI model was able to achieve the same functionality as the original human author of the PR.

Our design philosophy when creating the agent scaffold optimized for updated Claude 3.5 Sonnet was to give as much control as possible to the language model itself, and keep the scaffolding minimal. The agent has a prompt, a Bash Tool for executing bash commands, and an Edit Tool, for viewing and editing files and directories. We continue to sample until the model decides that it is finished, or exceeds its 200k context length. This scaffold allows the model to use its own judgment of how to pursue the problem, rather than be hardcoded into a particular pattern or workflow.

SWE-bench doesn't just evaluate the AI model in isolation, but rather an entire "agent" system. In this context, an "agent" refers to the combination of an AI model and the software scaffolding around it. This scaffolding is responsible for generating the prompts that go into the model, parsing the model's output to take action, and managing the interaction loop where the result of the model's previous action is incorporated into its next prompt. The performance of an agent on SWE-bench can vary significantly based on this scaffolding, even when using the same underlying AI model.

There are many other benchmarks for the coding abilities of Large Language Models, but SWE-bench has gained in popularity for several reasons:

Note that the original SWE-bench dataset contains some tasks that are impossible to solve without additional context outside of the GitHub issue (for example, about specific error messages to return). SWE-bench-Verified is a 500 problem subset of SWE-bench that has been reviewed by humans to make sure they are solvable, and thus provides the most clear measure of coding agents' performance. This is the benchmark to which we’ll refer in this post.