100% disagree with this take, the flexibility in controlling the prompt leads to QwenCoder2.5-32b outperforming gpt-o1 and claude sonnet 3.5 for nearly everything that I use it for (true for Gemma-27b and llama3.3-70b, though in this context I'm almost always using the former).
A specialist model that's specifically prompted to do the correct thing will outperform a SOTA generic model with a one size fits all system prompt. This is why small autocomplete models can very obviously outperform larger models at that specific task.
I am speaking 100% from experience and ignoring all benchmarks in forming this view btw, so maybe it's just my specific situation.
Also, in general I don't find the difference between SOTA models and local models to be that significant in the real world even when used in the exact same way.
Also, in general I don't find the difference between SOTA models and local models to be that significant in the real world even when used in the exact same way.