What if we create a benchmark that works like this and assigns ELO scores? Models fight head-to-head by writing a question, a bug, or an incomplete implementation, which the opponent has to answer, fix, or finish.
https://en.wikipedia.org/wiki/Generative_adversarial_network
https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu...
In class you'd probably want a rule saying at least one LLM should be able to figure out the answer, but in a head-to-head I'm not sure how to solve it.
On the other hand then maybe a good strategy would be to write questions that the LLM just happen to have in a nich dataset in its training ”what did user5455 say to user6835?”
Nevermind my idea.
I suspect LLMs can do this just fine and probably better than us, but they do need to be trained specifically for it and I have a hard time coming up with good sources of training data for it.
I don't think either harnesses do enough to encourage the model to challenge all assumptions and ask questions, maybe because users might find it annoying. That step is basically a requirement IMO.
I've found all of the GPT-5 models to be very nit-picky, useful for code review and mathematics (important for my work), but seemingly gets in the way of "aesthetic" code, e.g. overly defensive code to cover all edge cases, even if unlikely.
There is seemingly also a tradeoff between flexibility vs instruction following. In my experience Opus will sometimes ignore instructions but can "fill in the blanks" more, vs GPT-5.5 follows instructions better but perhaps at the cost of rigidity.
Because you'd not want to forever loop outside your home when asked to "while you're out, grab some eggs" :)
Because the entire reason we use LLMs is to supposedly improve productivity?
Specifying the problem is not extra work separate from solving it. If you skip that step, the ambiguity gets pushed into the model’s assumptions. Then you get a plausible looking answer to the wrong problem and have to waste time backing out of it.
LLMs are not magic machines that can read your mind.
In my own work, it's usually been a few critical assumptions the model made silently (and I never even though of initially) that end up being the difference between passable results the first try, and me having to go back and fix things. Occasionally some questions force me to rethink the problem entirely.
I basically always begin any long-running session with this kind of brainstorming. I don't find the existing plan modes in Claude Code/Codex to be critical enough.
Minimizes effort, is the obvious answer.
I think the reason claude has so much mindshare is exactly because it’s more useful to non-developers who wouldn’t know how to describe what an api call executes to his grandmother.
For those who can, I can’t find much of a difference between them. Codex has the slight edge, but that’s all just “feels” to me.
> I think the reason claude has so much mindshare is exactly because it’s more useful to non-developers who wouldn’t know how to describe what an api call executes to his grandmother.
This is exactly the benefit for most people.
Most people don't want to code the app, they just want the app.
Even people like us who do like coding, we can only think of all of these things within a domain that we already know; somebody who writes shaders for games isn't likely to know or care much about the ins and outs of database development or how healthcare privacy law and KYC interact with zero-knowledge proofs.
(Of course, if the AI knows about these things and then completely fails to make use of that knowlege, that's still a fail).
Its not my experience Opus is leagues ahead or even superior, but in any case, since GPT 5.5 has Instant, Medium, High, Extra High and Pro...Should the comparison be with GPT on Pro, instead of Extra High as it seems to be the case in the table?
There's specific tasks that Opus does better on like Frontend Dev and Design but for anything else 5.5 just laps it.
You guys are all a lost cause.
4.8 also requires more than one prompt but its output is significantly higher quality and offers more insight
Fable 5 is a different beast however.
At a high level. It misses low level or other non-functional requirements differently so I wouldn't say Opus is just strictly better.
It's also possible that it's just a harness problem more than model.
Any decent benchmark would use the whole of TRIZ to generate a giant ball of a problem first and watch a AI deduce a optimal solution.
I wonder if a model could score higher if it had a human at its disposal?
I'm more interested in how fable would do
I don't know what a better approach would look like while still remaining feasible, however this approach of telling a LLM to make a subjective judgement seems fundamentally flawed.
This is common in system prompts and frames the responses.
For example, you'd get different responses saying:
1. you are a pirate writing sea shanties about programming;
2. you are a news reporter writing an article on physics;
3. you are a senior software engineer with complete knowledge of PostgreSQL.
For 1 you could get responses along the lines of the Wellerman sea shanty -- "There once was a program that was set to C ...".
The "make no mistakes" bit does look dubious. It would be interesting comparing the results with and without that bit and trying alternative ways of getting the same desired behavior.
Which LLM should we even use to judge taste? Is it giving an unfair advantage to Model X if we use Model X as the judge? Maybe we should use multiple models as the judge, but now the model that's best at recognising and praising its own code has an advantage. The whole thing is just an unsolvable problem when a LLM is the judge.
There have been studies that showed that models tended to rate responses from their own family of models better than equivalent responses from other families, eg. gpt-4 would prefer a response from gpt-3
Of course, no-one seems to be (publicly) doing the comparative measurements that might allow us to reach rational conclusions here.
The advice I've heard is to emphasize the traits you want, not discourage the traits you don't. So rather than saying "make no mistakes" you can do something like you suggested with writing it as "check your work" or "ensure you answer correctly and concisely".
To me this already disqualifies the benchmark. That statement is missing the most critical piece about senior engineers: the senior engineers know how to obtain input for their work on their own whether that talking to customers or using metrics. Never ever they come up with stuff on their own - that’s junior behaviour.
Until a coding agent will be able to *gather* the input on its own, its never going to be „senior”
The real skill is being able to both pull the necessary information from these sources as well as being able to intuit gaps in that knowledge based on their understanding of the business and their domain expertise & wisdom. Sometimes you can't get a perfect picture, sometimes the people who should know aren't able to tell you what they really need. You still need to do the right thing.
A benchmark like this can potentially do the second part. But I don't think any model would be good at it, for now.
What you really need is an objective benchmark
But for a benchmark with the goal of picking a model to replace a human on some task? I really think the human should judge which is best.
I haven’t gotten very far yet but I had an idea for a personalized benchmark tool that walks through your git history and helps you craft prompts for tasks that bugs or features already implemented by hand so you can compare how different LLMs would do it.
I'm investigating/experimenting with using traditional NLP (stanza, spaCy, etc.) to try and grade the responses according to different metrics (is the response in first/second/third person?, is it written as poetry, prose, or drama? etc.). I'm also thinking about using information extraction and synonym detection to handle data queries and the like.
And LLMs have gotten good at handling these issues. There is asymmetric difficulty in generating a solution and verifying it correct. And overtime LLMs are getting better and better which allows training on synthetic data to make it better.
But seriously, as an industry we're terrible at assessing engineering levels, I've worked with "senior engineers" who can't code and I've worked with "junior engineers" who could run rings around them.
Benchmarks like this should be much more precise about what they're actually testing, and what axes they're hard on. We also need to rise above prompts like "you are a senior engineer", it's woo, and it's far better to ask for precise outcomes.
Of course, it's impossible to know for sure what was LLM processed or not, but some of your posts (like this one) are getting classified that way.
But it's a lie. Nobody's paying you to make paintings. They're paying you to build machines. The comparison between "making working software" with "taste" always devolves into bikeshedding and subjective opinionism, uses subjective human feelings to describe what should be objective and functional, isn't rooted in scientific rigor, and detracts from the real purpose of the thing. The work doesn't actually get better by trying to apply artistic principles to engineering. It just feels better for the people making it.
Once you make the machine work, then you can go about gilding the lily. But this is unromantic, unsatisfying, boring. Since the inmates run this particular asylum, we end up with a benchmark that tries to accurately mimic the human ego as applied to software design. Thus the new Gods create their digital Adams and Eves in their image.
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.
That’s the reason why I buy Apple products in private, because I value the design over the exorbitant prices they charge; and it’s the reason why I mull over code that’s already functional until it’s pleasing my ideas of elegance.
I can come up with all kinds of justifications and explanations why the code I’ve written a certain way is objectively better too - understandability matters to the next guy after all - but I won’t be ashamed for taking a certain pride in my work, even if nobody other than me ever values it. That’s fine.
When the LLMs finally take over coding altogether, you’ll have your raw, functional code. Won’t be long anymore. But for now, I’m a human, and I will do human things.
"Does it work" glosses over a bunch of things: is it fast, cheap, secure, reliable, easy to understand, easy to modify? And that's just for server software where you've nailed down all the functional requirements. Determining what the functional requirements is it's own question.
And all these other non-happy path requirements are somewhat in tension with each other, so what is ideal in one environment is not necessarily ideal in another.
And in particular, "easy to understand/modify" is truly subjective. Different people have different ideas of what easy to understand means. Even if we get to a world where AI is writing all our code, "easy to understand/modify for the AI" is still an important question. We've probably all seen prototypes that collapse under their own weight of slop by now.
This "standard" exists for the sake of code analysis vendors to be able to have some sort of shared taxonomy, but also provide a fig leaf of standardization to their products.
Personally I've always been a proponent of project specific standards, but after many years of discussions about more or less individual preference I've come to think that maybe settling on something global isn't the worst idea. Not that I think it must be this one in particular, but it's not the worst start either.
If you want to express your substantive points thoughtfully, that of course would be fine.
If you'd please review https://news.ycombinator.com/newsguidelines.html and stick to the rules when posting here, we'd appreciate it.
For any professional work you care about the details.
Even for hobby work, if you are using LLMs then presumably it is to do the drudge work of coding, not making the decisions, and that goes doubly so if you are a senior developer. Sure the LLM can "fill in the details" and vibe code (or attempt to) you a compiler or whatever, but the whole reason you are doing a hobby project is presumably because you want to bring your experience to bear and build a GOOD compiler, not a generic one.
(Full disclosure, I'm not a software engineer.)
Then maybe you should abstain, because your comment is a complete load of nonsense.
Bad code is bad code regardless of the history or scope of the feature. Maintainability is important because you can never know if a feature will be built upon in the future or not.
Bloat is bad regardless, because it increases the overall complexity of the whole software development lifecycle, for the whole team, forever (or until refactored out): It's harder to keep track of the code and how it works to write new requirements, it's harder to write, it's harder to read and review, it's harder to debug, etc.
You can write extremely poor code that has no bugs, it doesn't make it tasteful. This is simply a ridiculous statement.
Of course maintainability is important. It's almost like saying good code is important (duh). The issue is that what is or isn't maintainable depends on the problem at hand. Sometimes you need to build heavier abstractions or refactor existing code when implementing a feature because it will pay off later. Other times, that exact same approach is horrible over-engineering because a simple, direct fix was all that was needed, so in fact you introduced a maintenance burden. You cannot reliably decide whether a patch is "bloated" or "tasteful" when looking at a diff without knowing where the project is headed.
>You can write extremely poor code that has no bugs, it doesn't make it tasteful.
You can, but it becomes increasingly hard to do so as you try to add features and maintain it. Taste, whatever that is, should ultimately lead to a measurable increase in the quality of the final product; if it doesn't, then your definition of "taste" is irrelevant. What I'm proposing is to skip trying to measure this ill-defined concept and only assess the quality of the final product, after the agent spent a significant amount of time working on it, and a reviewer spent a significant amount of time testing it. Agents should be assessed on their ability to build entire projects (e.g., many large features or even an entire app), not just a single feature. If an agent has no taste, then its bad decisions will compound and result in it stalling, or its output having more bugs and performing worse, given a sufficiently large scope.
A few months later you find out it is made of PU foam and printed waxed paper. A misplaced knee could bring it down. It’s likely to completely fall apart in a year. Is that irrelevant?
You can't test if a codebase will be extensible or maintainable as requirements change in the future, if the abstraction level or architecture is sound - that's down to code quality measures like the ones used here. LLMs are very good at slightly cheating to pass tests even when the implementation is wrong. Introducing subjectivity - the kind of input a human will provide - leads to improved output.
https://senior-swe-bench.snorkel.ai/blog/2026-06-16-how-it-w...
If the benchmark is to implement features that are part of an open source project, and LLMs have those changes as part of their training dataset, it seems that they could just give a verbatim or slightly modified version of the change in their training data.
And if one updates the benchmark to only incorporate code changes that are past the models knowledge cutoff, then the benchmark is less comparable over time, since the changes in the benchmark at time T and T+1 aren't the same.