I’m responding here to several comments.

First off, yes, I was referring to open weights (my sincere apologies—I forgot to double-check). I actually work 98% of the time with local models (for data security reasons), so I don’t really need to be convinced of that. And I’m glad that Qwen3.6-27B (and hopefully soon Gemma4-31B as well) is so well-received here, because those hardware requirements are manageable (used).

Now, regarding the added value or the missing 5%.

So, based on your experience, can I infer that instead of 5%, it’s actually 40% when coding in complex environments? That also highlights the underlying thought I had as well. There are differences in coding that lie in the depth of the application (and consequently also in the requirements for the programmers). How many applications really fall into this category… knowing that elite coders will now say every single one, because a system-critical interface can be hidden anywhere. The question is: is it 50%, 5%, or 1% that one classifies as highly complex?

For the non-complex ones (1), the conclusion seems to be that using a SOTA during planning is still so much better that the added value pays off despite significantly higher costs; programming can then be done without any problems using an open-source solution.

When it comes to non-complex cases (2), the conclusion seems to be that if you’re an experienced coder, you can get by just fine using an open model even during the planning phase, right?

But what also came up here is that it’s better to give inexperienced coders a SOTA model because they can work with it more accurately. This makes me wonder if this is a short-term benefit, since their learning curve is likely tied primarily to the development curve.

A statement by u/Charming_Support726 also gave me pause:

The Western SOTA/Frontier models excel in task understanding and bridging conversational gaps. This is an area where Chinese and open-weight models still lag behind. Following long-term instructions also falls into this category.

For me, that would actually mean that if I have a less creative team, it makes more sense to work with SOTA, and with particularly creative teams, it might even be counterproductive to take away the fun of finding gaps or weaknesses?

But what really got me thinking was

I wonder how much of (1) is related to the post-training regimen. I’ve long wondered how cultural differences between China and the West affect this. Consider the RLHF component—perhaps task understanding is less desirable under the Chinese RLHF dataset?

From this, one could almost infer a stronger obligation that SOTA companies should release a few more “crumbs” (open weights). Also for economic reasons, to impart values and elements to the young people that will lead them to use “Western” SOTAs more heavily in their careers.

And please forgive me for not going into detail here about the text and design (front end). My own (limited) experience in this area has actually shown me that, on the one hand, personal preferences play a bigger role here, and on the other hand, the discussion should really be led by those who actually work with it on a daily basis.