71.2% puts it at 5th, which is 4 points below the leader (four points is a lot) and just over 1% lower than Anthropic’s own submission for Claude Sonnet 4 - the same model these guys are running.
But the top rated submissions aren’t running production products. They generally have extensive scaffolding or harnesses that were built *specifically for SWE bench*, which kind of defeats the whole purpose of the benchmark.
Take for example Refact which is at #2 with 74.4%, they built a 2k lines of code framework around their agent specifically for SWE bench (https://github.com/smallcloudai/refact-bench/). It’s pretty elaborate, orchestrating multiple agents, with a debug agent that kicks in if the main agent fails. The debug agent analyzes the failure and gives insights to the main agent which tries again, so it’s effectively multiple attempts per problem.
If the results can be reproduced “out-of-the-box” with their coding agent like they claim, it puts it up there as one of the top 2-3 CLI agents available right now.
But let's say a group uses it as a metric as part of CI and each new idea / feature they create runs against SWE bench. Maybe they have parameterized bits and pieces they adjust, maybe they have multiple candidates datasets for fine tuning, maybe they're choosing between checkpoints.
This will also end up overfitting - especially if done habitually. It might be a great metric and result in a more powerful overall model. Or it might not.
https://huggingface.co/datasets/princeton-nlp/SWE-bench_Veri...
Its up to your retrieval system/model to selectively hunt for relevant context. Here's a few critiques of the benchy:
Building multiple attempts into your agent is stretching the rules, even if technically it’s acceptable
I.e. the agent cannot even know which tests are failing.
It has to both fix the issue based just on the issue text and fix it in the specific way the unit test, which it cannot see, expects.
For this reason I find the benchmark a little disconnected from the reality of software engineering.
Another approach might be the LiveBench approach where new tests are released on a regular basis.
The approach is to use workloads defined by developers and end users (not providers) that reflect their real-world tasks. E.g. in finance, delivering market snapshots to trading engines. We test full stacks, holding some layers constant so you can isolate the effect of hardware, software, or models. Every run goes through an independent third-party audit to ensure consistent conditions, no cherry-picking of results, and full disclosure of config and tuning, so that the results are reproducible and the comparisons are fair.
In finance, the benchmarks are trusted enough to drive major infrastructure decisions by the leading banks and hedge funds, and in some cases to inform regulatory discussions, e.g. around how the industry handles time synchronization.
Now starting to apply the same principles to the AI benchmarking space. Would love to talk to anyone who wants to be involved?
I could understand focusing on a niche business use case, but coding is a main focus of the foundation models themselves.
I think that the next step is getting an official "checked" mark by the SWE bench team
I do not want to pay API charges or be limited to a fixed number of "credits" per month.
I updated to the latest version last night. Enjoyed seeing the process permission toggle (rwx). Was a refreshing change to keep the security minded folks less in panic with all the agentic coding adoptions :-)
The best submission is swe-bench-multilingual is Claude 3.7 Sonnet which solves ~43% of the issues in the dataset.
https://news.ycombinator.com/item?id=44833929, my comment https://news.ycombinator.com/item?id=44835939
