I think it depends on the problem space, and to a large extent the languages used. I have had the opposite experience with GPT.

I have found GPT to be pretty awful at writing Elixir, and dealing with more open ended tasks. Opus is (for the moment) better at writing Elixir, and Fable is much better than either at actually grokking a problem space. I had switched to GPT for about six weeks and eventually stopped using it altogether after it relentlessly gaslight me. Those six weeks using it were essentially completely wasted work.

Right now I’ve moved to a mix of Opus/Fable and Deepseek Pro. Deepseek is far and away the most cost effective, fast, and quite good for 90% of implementation work. Deepseek really only falls apart (in Elixir) when it tries to use runtime features at compile time; which, for the initial lift of a project I have was a bit painful. Fable was able to pretty quickly get things sorted and now things are progressing nicely.

With all that said, I think the various models have unique strengths that are sometime hard to uncover. I also don’t see a world, at least in the near future, where I’ll only use one model. I’m happy Anthropic continues to push the frontier forward. I’m more than happy to trade “efficiency” for quality when I need to.