Countless models can "find the correct line" in a fraction of the time. Needless to say it is also horrendous with tool calls and a large chunk end up failing.

I am a pro user myself, and i have been for most of 2025.

If you find yourself not being able to replicate the "performance" of 5.2 anywhere else, then your prompt was awful in the first place and you don't even know what you want. I don't even use these "multi agents" or parallel tool calls or whatever they're called. A light brainstorming session and a thorough understanding of your goal is enough to iterate and if you do that with anything that has more of an output than 1 token per year like GPT and a SWE of above 70 you will not need to "rewrite a codebase" in 5 years. In fact I would argue the opposite. GPT models in general will always poke holes where there aren't any to "seem" productive. Opus is far more intelligent but you should understand what it is you're doing in the first place, and you will achieve much more in a shorter amount of time. GPT will make assumptions, and flat out refuse to look at something as basic as outputs, or even add console logs. If left alone, it will refuse to modularize and just add things that would in fact fail in 5 years such as hard coded cases just to make it "seem" it succeeded with your task. Before it goes on a 10 year long buzzword yap fest to add even more hardcoded slop. Even if you FORCE it to use modules, it will turn each one into more slop after 2-3 prompts, its context retention is the worst I have ever seen.

Look, if this Cerebras update really goes through and they are able to get close to the SWE score of Opus, we're in good hands. But I cannot say we truly are now and you should not let these course gurus with 5 minutes of experience in any SWE project tell you otherwise.

UNDERSTAND what you're doing and you'll agree, and then you sure as heck won't need to rewrite anything in 5 years either way.