Why? Because what was built with an open model can be sneezed into existence by a frontier model ran via first party API with the best practice configurations the providers publish in usage guides that no one seems to know exist.
The difference between the best frontier model (gpt-5.4-xhigh or opus 4.6) and the best open model is vast.
But that is only obvious when your use case is actually pushing the frontier.
If you're building a crud app, or the modern equivalent of a TODO app, even a lemon can produce that nowadays so you will assume open has caught up to closed because your use case never required frontier intelligence.
You can run it on your own hardware, with perfectly predictable costs and predictable quality, without having to worry about how many tokens you use, or whether your subscription limits will be reached in the most inconvenient moment, forcing you to wait until they will be reset, or whether the token price will be increased, or your subscription limits will be decreased, or whether your AI provider will switch the model with a worse one, and so on.
Moreover, no matter how good a "frontier model" may be, it can still produce worse results than a worse model when the programmer who manages it does not also have "frontier intelligence". When liberated of the constraints of a paid API, you may be able to use an AI coding assistant in much more efficient ways, exactly like when the time-sharing access to powerful mainframes has been replaced with the unconstrained use of personal computers.
When I was very young I have passed through the transition from using remotely a mainframe to using my own computer. I certainly do not want to return to that straitjacket style of work.
