There is no clear answer. It's debatable among experts.
The grandparent post seems to believe that the issue is algorithmic complexity and programming aptitude. Personally, I think that all the major LLMs are using the same basic transformer architecture with relatively minor differences in code.
GPT is trained on more data with more parameters than any open source model. The size does matter, far more than the software does. In my experience with data science, the best programmers in the world can only do so much if they are operating with 1/10th the scale of data. That applies to any problem.
The grandparent post seems to believe that the issue is algorithmic complexity and programming aptitude. Personally, I think that all the major LLMs are using the same basic transformer architecture with relatively minor differences in code.
GPT is trained on more data with more parameters than any open source model. The size does matter, far more than the software does. In my experience with data science, the best programmers in the world can only do so much if they are operating with 1/10th the scale of data. That applies to any problem.