Astra writes well (which is why I often like to work with it over Claude), but it tends to produce code golf that's borderline unreadable, like completely omitting line breaks. I thought I had something funky with my config, but it seems to be a known issue.
The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc?
I use the frontier models only in plan mode; coding is held out to cheaper models.
I run Paseo as an orchestrator with agent profiles. "Distinguished Member of Technical Staff" -> Fable, plan mode. "End-User Customer Advocate" -> Astra, plan mode. A few others. Basically, these agents (with profile prompts) can only be "consulted" for input on design elements; it's OK if they produce a bit of verbosity here. This has worked fantastically for me, really awesome to see a self-constructed team own the work end-to-end.
A "Director of Engineering" (6.1 Sol or Opus 5.5) also in plan mode organizes the team for the task and then owns coordination and instruction. You can think of this in essence like a "model router".
Coding is handled by Haiku 5.5/GPT 6 Luna/DeepSeek v4.1 Flash. One "Principal Engineer" Sonnet 5.5 or 6.1 Sol for some tasks.
I think it's the best possible approach. It lets you refer to content that's unfortunately stuck on the site, while still signaling clearly that it's something to avoid if possible and to apologize for if not. Omitting the link just removes information without influencing consensus on whether to use X.
edit: I wish it would let me view the darn screenshot tho :c
I've noticed the line breaks issue. It's easily fixed with a formatter like prettier and, frankly, that kind of quality-of-life thing is best handled deterministically anyway.
GPT 5.5 on Medium is all I use lately, I don't understand why anyone would pay drastically more for slightly more, when Opus has been good enough for a year now to do most things.
I have a simplify skill built from a rich hickey talk that basically solves the erosion and verbosity metrics SCB flagged (i.e. erosion and verbosity are way better and improve over time). However, after running the SCB benchmark I cannot find any evidence that it actually improves test pass rate trajectories. Food for thought on maintainability metrics.
Is there a reasonable way to run a representative subset myself?
Edit:
> For folks who want to try to repro these findings: I used the pinned v1.0 catalog, the stock just-solve prompt, seed 42, the any-case runner pass policy, and Docker with Python 3.12 and uv.
For most things I find medium to be the highest quality output. However if I want it to do spatial reasoning or if I want it be funny / write something (not code) of almost-passable quality, then xhigh is where it's at.
I believe effort can mean both "solve harder problems" and "produce more stuff". I mostly don't want more stuff than I asked for, so a capable model at lower effort can indeed be the wiser choice. Faster response too.
This is interesting. Is nobody training LLMs to repeat resolving issues by patches from real projects where refactoring is required, judging the refactor similarity and not just tests passing?
Secondly, I’m mostly using Astra Ultra or Sol 6.1 when tight on quota. I pay $200/month and rarely if ever run out. Am I doing something wrong? Or perhaps I’m not that productive as you guys?
Same here. I’m feeling productive enough. If I was more “productive”, I wouldn’t be able to keep up with what I was producing. My attention and cognitive broadband is the bottleneck. I find it hard to believe that people are actually productive using 4 active chat windows simultaneously for 8+ hours a day.
I find QFN on xhigh usually overruns the context window for no good result. If it can do the task, it'll do it quicker on medium. If it can't do the task, xhigh won't change that.
Astra writes well (which is why I often like to work with it over Claude), but it tends to produce code golf that's borderline unreadable, like completely omitting line breaks. I thought I had something funky with my config, but it seems to be a known issue.
https://lucumr.pocoo.org/2026/9/7/astra-why/
[Sorry for X] https://x.com/tenobrus/status/2096636223414808719
The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc?
I use the frontier models only in plan mode; coding is held out to cheaper models.
I run Paseo as an orchestrator with agent profiles. "Distinguished Member of Technical Staff" -> Fable, plan mode. "End-User Customer Advocate" -> Astra, plan mode. A few others. Basically, these agents (with profile prompts) can only be "consulted" for input on design elements; it's OK if they produce a bit of verbosity here. This has worked fantastically for me, really awesome to see a self-constructed team own the work end-to-end.
A "Director of Engineering" (6.1 Sol or Opus 5.5) also in plan mode organizes the team for the task and then owns coordination and instruction. You can think of this in essence like a "model router".
Coding is handled by Haiku 5.5/GPT 6 Luna/DeepSeek v4.1 Flash. One "Principal Engineer" Sonnet 5.5 or 6.1 Sol for some tasks.
Don’t apologize for x, just don’t post x links.
I think it's the best possible approach. It lets you refer to content that's unfortunately stuck on the site, while still signaling clearly that it's something to avoid if possible and to apologize for if not. Omitting the link just removes information without influencing consensus on whether to use X.
edit: I wish it would let me view the darn screenshot tho :c
There are plenty of alternatives to link to X content without hitting X's servers.
I've noticed the line breaks issue. It's easily fixed with a formatter like prettier and, frankly, that kind of quality-of-life thing is best handled deterministically anyway.
xxcancel (n.b. dos equis) works:
https://xxcancel.com/tenobrus/status/2096636223414808719
GPT 5.5 on Medium is all I use lately, I don't understand why anyone would pay drastically more for slightly more, when Opus has been good enough for a year now to do most things.
Why not GPT-{5.6, 6, 6.1} Sol? They're all cheaper than GPT 5.5, and as "smart", if not "smarter", at least to me.
Though, it has been a while since I've used 5.5.
I have no data to support this, but when working on SaaS systems I find anthropic models more effective.
But for webdev, deep research, code review, and a few other tasks I find the current 6.1 sol better.
But I use both. And I use codex more.
Surely they mean Opus-5.5...
I have a simplify skill built from a rich hickey talk that basically solves the erosion and verbosity metrics SCB flagged (i.e. erosion and verbosity are way better and improve over time). However, after running the SCB benchmark I cannot find any evidence that it actually improves test pass rate trajectories. Food for thought on maintainability metrics.
Is there a reasonable way to run a representative subset myself?
Edit:
> For folks who want to try to repro these findings: I used the pinned v1.0 catalog, the stock just-solve prompt, seed 42, the any-case runner pass policy, and Docker with Python 3.12 and uv.
And https://github.com/gabeorlanski/scb-problems/
For most things I find medium to be the highest quality output. However if I want it to do spatial reasoning or if I want it be funny / write something (not code) of almost-passable quality, then xhigh is where it's at.
Wouldn't it be better to enforce pass via the harness and then measure cost/quality since that's more what people are likely to do?
Great benchmark!
I believe effort can mean both "solve harder problems" and "produce more stuff". I mostly don't want more stuff than I asked for, so a capable model at lower effort can indeed be the wiser choice. Faster response too.
This is interesting. Is nobody training LLMs to repeat resolving issues by patches from real projects where refactoring is required, judging the refactor similarity and not just tests passing?
Are the raw outputs published somewhere?
I am currently investigating SCB results and a larger, more representative dataset to work on would be really helpful!
First of all, what happened to Max and Ultra?
Secondly, I’m mostly using Astra Ultra or Sol 6.1 when tight on quota. I pay $200/month and rarely if ever run out. Am I doing something wrong? Or perhaps I’m not that productive as you guys?
Same here. I’m feeling productive enough. If I was more “productive”, I wouldn’t be able to keep up with what I was producing. My attention and cognitive broadband is the bottleneck. I find it hard to believe that people are actually productive using 4 active chat windows simultaneously for 8+ hours a day.
Related. Others?
Benchmarking Opus 5 on SlopCodeBench - https://news.ycombinator.com/item?id=49076391 - July 2026 (119 comments)
Opus did better on medium? Can anyone confirm this somehow? What’s the sweetspot in your opinion?
https://www.reddit.com/r/LocalLLaMA/comments/1xatxqk/qwen38f...
Recent evaluation result.
I find QFN on xhigh usually overruns the context window for no good result. If it can do the task, it'll do it quicker on medium. If it can't do the task, xhigh won't change that.
why measure something you can just enforce with something like opcore which we have built? https://github.com/the-open-engine/opcore/
Finally a serious benchmark!
I wish a company named human layer won't write their reports with so much Claudisms.
You expect an AI company to slow down and care about what they produce? How naive are you?
If you haven't pushed so much that people are physically unable to look at it, are you even running a company?
I think people downvoting you missed the sarcasm.
On the contrary.
The irony is the article is very sloppy. Can we at least write it and review it properly..
This is true. I was unfamiliar with this concept (I use codex/claude daily), and am trying to get to the "bottom line".
Oh, it's there at bottom under "my takeaway". But I still didn't get it initially.
Slop Code Bench? Need a Slop Writing Bench too
What's wrong with it? Reads fine to me.
There are 36 tests. You can pass 0 or 36 or any number inbetween. Lets give five significant digits of how accurate solving was.
That is not how data physics work. It should not be how data science works.