This isn't true. Benchmarks do not define the usefulness of the models. Mixtral is much more useful for me right now than GPT-4. Look at LLaVA, or the new one, and the very impressive LWM (the text-only version is LLaMA2-based with a 512K context, which does not require TPU to run the inference). The fine-tuned LLaMA 34B is much faster than GPT-4 Turbo and less annoying, providing pretty impressive quality of results.
Who mentioned anything about benchmarks? YMMV, it depends on exact use cases, from my extensive testing using my cases, GPT-4 Turbo still is much easier to direct than any of the others you have mentioned.