Benchmarking Opus 5 on SlopCodeBench

SCB is definitely an underrated benchmark. For me the unique selling point is that it more closely mirrors software development by not stopping after a single task. The agent has to keep code clean. The only disadvantage is all the problems are greenfield and not git inited so the agents don’t make use of git diffs.

I’ve used SCB as part of my assessment of agent skills (superpowers, GSD etc) https://orcabot.com/labs/do-skills-improve-coding-agent-accu...

There is a small but growing community on discord for discussing SCB so if interested please join https://discord.gg/BrC4BA9sVj

I always have Claude recite a pledge before starting coding to fix redundant code it notices over time. It does seem to find redundancies, but only when I point out bugs, that's when it goes into fixing mode and actually applies my Don't Repeat Yourself preference from the CLAUDE.md.

The original paper cited by this post does try to see if improved prompting will make a big difference in the end using a `plan_first` prompt variant, but find no influence on pass rate at the end of the benchmark. The `plan_first` seems to assume coding agents will just refactor once they finish features, but I don't think they tend to refactor significantly unless they are told to fix bugs rather than build features. The benchmark leaves tests hidden with no fail-to-pass feedback, so that may be why degradation is monotonic.

Nice! I actually ran across this paper+benchmark recently, too. It's the first I've found that start to aim at some of the non-functional and longitudinal requirements that I think have always been an important part of writing production code.

It's especially relevant now that models are good enough to solve ~most point-in-time problems.

Some relevant but disconnected thoughts:

- deterministic scores are so nice

- what "maintainable" is is probably some high dimensional space described by these signals; it'd probably require some human labeling to figure out where this space is

- another signal I've been thinking about and I'm seeing increasingly get brought up is the state space of a system; I'm seeing formal methods pop up a lot recently

I hope the big labs will start using this benchmark in their RL pipelines. Reducing complexity in generated code should be the number 1 priority, in my opinion. The holy grail for me is models implementing features while reducing LoC (i.e., choosing the right abstractions).

What is also nice about this benchmark is that it can be used to iterate on prompts/skills for reducing code complexity.

This is nice, but would be really useful measured against human performance, and yes - I understand that is likely a difficult challenge.

Many people though are going to read the headline figures and think it means - say - Opus 5 is only a quarter the strength of a human coder.

This matches my experience of Opus 5 being a nice improvement over Opus 4.8, but not being revolutionary like Fable felt.

I’ve now replaced my use of Opus 4.8 xhigh with Opus 5 medium, and I’m using less tokens and it’s quicker. I can understand people being annoyed by its writing style but for getting work done that really doesn’t bother me. I’ve been really enjoying using it.

So far my 'solution' to this has been periodically run a separate round of whole-codebase code review (preferably Fable) and then rounds of refactoring off the results of that

I may be crucified for asking this but: is there any proof that slop matters beyond our sensibilities as developers?

If the code is ugly, but defects are low—does it matter?

If the code is hard to read, but clients are happy—should I care?

Genuinely asking. Weird times we live in.

I'd be curious to see the raw test results.

I have a suspicion that most models will miss the `database_migration` Checkpoint 2 test that includes a `default_value` because it could be interpreted as either a JSON-literal or a SQL-expression.

There might be other tests as well that are prone to failure for reasons other than the reasons cited in the paper.

I think a cool experiment would be to adjust the order of the features implemented (e.g. checkpoint 3 then 2 then 5 then 4) where dependencies allow it. Then one could account for some checkpoints being more difficult than others.

I haven't joined your chats in a while but glad to see you put this together, I truly feel as though opus 5 is not much of an improvement. The only time i ever felt a wow factor was opus 4, 4.6 and fable pre trump admin lobotimizing

I wonder to what extent we could steer performance on this benchmark, by providing an adversary model that penalises code duplication and overall lines of code?

Opus models were straightforward to review. But Opus 5 is very different.

Did you not benchmark latest GPT 5.6 or GLM 5.1/Kimi K3 because of cost? I can run them if you share how you ran them

Great writeup. The excessive function thing has always driven me crazy; I guard against this explicitly in Claude.md.

I have found that models are generally poor at managing refactors / complexity while also implementing new features. But I’ve had some success with a semi-lights-off approach where you decompose it and prompt the model adversarially in a second pass to look for new rough edges and areas of complexity or refactors that might simplify the codebase.

So I’d be very curious to see this benchmark but with something like a periodic “refactor turn” interleaved in.

Also eager to see Fable benchmarked; anecdotally that was the only model whose code I felt I could actually trust to not review closely.

To what degree is this a harness/system prompt problem? Models maybe should implement new stuff with as little impact on the existing stuff as possible by default? A simple system prompt for it to always check the code after task completion for proper simplifications, abstractions and cleanups before returning to the user? Instructions to retain "story like" readability of the code.