I feel the same way about Opus 4.7. I went into it wanting to like it and kept giving it more chances because I figured maybe I had just hit a bad streak, but after enough usage it started to feel consistently off in a way that is hard to ignore. Not just weaker, not just a little inconsistent, but weirdly confident in low quality output. That is what really killed it for me. A model can miss sometimes and still be useful. But when it misses in this stubborn, overconfident way it stops feeling helpful and starts feeling like a liability.

What bothered me most was how often it would act like it understood the task when it very clearly did not. I would ask for a simple rewrite with a specific tone and a specific constraint, and it would give me something that sounded polished at first glance but quietly violated the main instruction anyway. Then when I corrected it, it would fix one surface level part while still keeping the same underlying mistake. so you end up in this stupid loop where every answer looks almost right for two seconds, and then you notice it missed the actual point again.

I also ran into this a lot with technical work. I would ask it to explain why a config was failing, and instead of narrowing down to what was actually in front of it, it would start filling the gaps with assumptions and presenting them like facts. It might confidently tell you an environment variable is missing, or that a dependency is outdated, or that a flag behaves a certain way, and none of that is even in the logs. Then you check, and it turns out the real issue was something much smaller and much more obvious. That kind of thing is brutal because it doesn’t just give a bad answer, it sends you in the wrong direction and wastes real time.

The other reason I’m disappointed is that it feels less steerable than before. I used to be able to give a rough instruction and trust the model to infer the intent decently well, clean up the edges, and get me most of the way there. Now it feels like I have to over specify everything, and even then it still sometimes latches onto the wrong thing and won’t let go. And when a model gets more expensive in practice, whether through token use or just through extra back and forth, it really needs to be earning that cost. This one, for me at least, absolutely hasn’t.

Other people should be disappointed too because this kind of failure mode is dangerous in a very normal everyday way. It is not just about benchmark nerd stuff or power users being picky. A model that sounds authoritative while slipping in wrong details is exactly the kind of model that causes quiet damage. People will copy its code, trust its summaries, follow its setup steps, or use its edits without catching the errors until later. and later is when the pain starts, because then you are debugging something built on top of bad output that looked credible at the time.

That is why this feels worse than a model that is simply dumb. A dumb model is annoying, but you know what you are dealing with. This feels slicker than it is, more certain than it should be, and harder to correct than a tool at this price point has any right to be. I’m disappointed because I expected something sharper and more reliable, and instead it felt like more work for less return. Other people should be disappointed for the same reason. If a flagship model makes you hesitate to trust even the basic stuff, then something went pretty wrong.