I'm unable to find a local model that comes close to the effectiveness of GPT models in Codex, and I have 96GB of VRAM available and tried every local model under the sun so far. Neither of those you mention I'd say are good enough for day to day software engineering for me, but I'm also really strict about code quality and iterate on what outputs agents give me a lot before I'm happy.

With local models, this iteration cycle takes maybe 30 minutes for a single fix or feature, rather than 10 minutes with GPT+Codex, as there is so many corrections and iterations needed, although I will say that the speed I'm able to get locally makes it more fun that any of the remote models.

> although I will say that the speed I'm able to get locally makes it more fun that any of the remote models.

This is becoming increasingly important to me. Super smart max reasoning frontier is fine if I leave it running overnight on some prepared set of clearly defined tasks, but when I want to work with the LLM, throughput really matters, and I'll go with a dumber model to get there.

At some point though, it's fast enough and any speed gains beyond that just makes me the bottleneck.

I also am seeing the smaller models gaining big strides lately, closing the gap on frontier models (still a decent sized gap though). I don't even run the small models like Qwen 3.8 27B locally. I just try them out in the cloud to see how they are progressing, and I'm definitely able to be productive.