Claude SWE-Bench performance \ Anthropic

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.
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.
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.
The prompt outlines a suggested approach for the model, but it’s not overly long or too detailed for this task. The model is free to choose how it moves from step to step, rather than having strict and discrete transitions. If you are not token-sensitive, it can help to explicitly encourage the model to produce a long response.
The following code shows the prompt from our agent scaffold:
The model's first tool executes Bash commands. The schema is simple, taking only the command to be run in the environment. However, the description of the tool carries more weight. It includes more detailed instructions for the model, including escaping inputs, lack of internet access, and how to run commands in the background.
Next, we show the spec for the Bash Tool:
The model's second tool (the Edit Tool) is much more complex, and contains everything the model needs for viewing, creating, and editing files. Again, our tool description contains detailed information for the model about how to use the tool.
We put a lot of effort into the descriptions and specs for these tools across a wide variety of agentic tasks. We tested them to uncover any ways that the model might misunderstand the spec, or the possible pitfalls of using the tools, then edited the descriptions to preempt these problems. We believe that much more attention should go into designing tool interfaces for models, in the same way that a large amount of attention goes into designing tool interfaces for humans.
The following code shows the description for our Edit Tool:
One way we improved performance was to "error-proof" our tools. For instance, sometimes models could mess up relative file paths after the agent had moved out of the root directory. To prevent this, we simply made the tool always require an absolute path.
We experimented with several different strategies for specifying edits to existing files and had the highest reliability with string replacement, where the model specifies `old_str` to replace with `new_str` in the given file. The replacement will only occur if there is exactly one match of `old_str`. If there are more or fewer matches, the model is shown an appropriate error message for it to retry.
The spec for our Edit Tool is shown below:
In general, the upgraded Claude 3.5 Sonnet demonstrates higher reasoning, coding, and mathematical abilities than our prior models, and the previous state-of-the-art model. It also demonstrates improved agentic capabilities: the tools and scaffolding help put those improved abilities to their best use.
Claude 3.5 Sonnet (new)
Claude 3.5 Sonnet (old)
SWE-bench Verified score
Scores on SWE-bench Verified for some of our models, all using this agent scaffold.
For running the benchmark, we used the SWE-Agent framework as a foundation for our agent code. In our logs below, we render the agent's text output, tool calls, and tool responses as THOUGHT, ACTION, and OBSERVATION, even though we don’t constrain the model to a fixed ordering.
The code blocks below will walk through a typical case of the Sonnet 3.5 solving a SWE-bench problem.
In this first block, you can see part of the initial prompt given to the model,