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Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

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31–40 of 147 posts

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#31

So TL;DR benchmarking in a completely non-reproducible manner ? "Model X performed great, but we can't possibly tell you anything about the code it was looking at apart from it was a large code base from an unknown company". So basically pinky-promise benchmarking ? I'm not sure I follow the value here ?

In theory, as long as all the models are doing the same thing with the same tools, it's at least useful to see how they stack up against each other right now. It might not be great to track progress over time, as it can get benchmaxxed or the underlying resources may become obsolete.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#32

> Each task comes from a private production codebase that we licensed from a real-world company How does that work?

lot of ads everywhere offering to buy your codebase of real product/star up even it long gone or failed (offer usually price per lines of code). So most likely that they have bunch of abandoned codebases between small and medium sizes and probably also some fake codebases as well.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#33

I am trying to estimate if my reaction to seeing GPT-5.6 Sol last on that list is reasonable or or mostly emotional and find that I have no way of telling.

I do think it’s the wizard not the wand at this point given a decent model. These benchmarks don’t have the wizard.

Otherwise I wouldn’t see others in the exact same codebase struggle and underutilize agents while others thrive using the exact same ones.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#34
Does this mean they ended up sharing those private codebases with OAI, Anthropic etc? Also, the ~30% number tracks with my experience. I thought I was going insane for expecting too much from the models but they are still bad, including astra. This morning it messed something pretty trivial while fixing an issue which I was shocked to see. Also2, benchmarks don't mean much these days.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#36

I am trying to estimate if my reaction to seeing GPT-5.6 Sol last on that list is reasonable or or mostly emotional and find that I have no way of telling.

Sol failing mostly on “unverified assumptions” and rarely hitting “integration errors” seems about right to me. I think Sol is second only to Astra (and miles ahead of even Fable) in architecting & engineering the right implementation — but only if you are extremely specific and provide tight guidelines and guardrails. If you give it a one-liner… you’re going to have a bad (SHA-256-hash-verified) time.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#37

I am trying to estimate if my reaction to seeing GPT-5.6 Sol last on that list is reasonable or or mostly emotional and find that I have no way of telling.

I do think it’s the wizard not the wand at this point given a decent model. These benchmarks don’t have the wizard. Otherwise I wouldn’t see others in the exact same codebase struggle and underutilize agents while others thrive using the exact same ones.

In other words, we're still in the era of centaur chess.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#38

this is the closest benchmark to my experience using the model harness combo. Astra for as great as it is falls slightly behind Fable 5.1 for me for large feature work (although it comments code much better). in particular, Fable is able to assess priority better than Astra (meaning Astra sometimes does things that aren’t worthwhile while missing things that are clearly important, particularly on possible ballooning…

Do you find Fable significantly better than Opus at avoiding-overengineering? All of my recent testing of Anthropic models seems like they're tuned-to-hell to (a) be much slower than they need to be (running tests over and over during the loop vs at the end, say, even if those tests take a few minutes a pop) and (b) doing exactly that sort of "built a lot of fancy enterprisey feature-adjacent 'stuff'" even before nailing the actual feature. Sol and Terra both have some of the latter but they seem to do the actual work a fair bit faster (this may be a usage-based-priority-tier/rate-limit thing though) which helps offset it.

I think the bigco folks saw all the "it wrote all this code but the tests didn't pass" or "it wrote the feature but it's super brittle" and tuned the newer model+harness combinations incredibly aggressively to try to turn a lazy prompt into "median Enterprise Architecture design suggestions" to bring up the baseline, but in a way that slows you down if you don't want that.

I'm not on big enough subscriptions to want to burn a lot time just evaluating Fable/Astra comparatively until they're cheaper, heh. I can steer any of the cheaper ones just fine anyway.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#39

I think these benchmarks are not that useful, e.g. this suggests Fable is better than Astra, but in practice Astra is waaaaaay faster (like 5x; it's not even close), and also waaaay less annoying to talk to. There's only two or three sane options here - you can easily try them all and pick yourself.

I switched from Claude to Codex because Claude just doesn't do what you actually tell it to half the time. It dances around the edges and does busy work without actually tackling a tough problem.

I'm not sure what others are doing that they're getting such different results, but I'll take Codex every day of the week.

Re: Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

#40
post #25

The fact that gemini 3.8 flash is so high up there just tells you this is an awful benchmark. Try and use gemini 3.8 yourself for any real world work and you'll see it's terrible. It'll just go in circles reading the same file 20 times for no reason making hundreds of tool calls for a simple change. EDIT: I was using gemini cli... it's not a harness issue lol

Hard disagree. I use 3.8 flash in Antigravity a lot, and thoroughly prefer it to most Pro-class models. It's really fast, and I've had it make crazy progress on compiler-like problems that previous models including Opus simply failed at. On ultra plan you can have it going for hours, and make incremental progress with good prompting for review interrupts. It solved a problem I couldn't solve for weeks in under 6 hour…

This is my exact experience with the model - https://x.com/ThePrimeagen/status/2095565354726502683

And it just BURNS tokens like crazy.

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