It would appear that OpenAI is of a similar mind to me in terms of how best to qualitatively demonstrate model capabilities. Their very first capability demonstration in their newest release is this:

In terms of actual performance on this 'benchmark', I'd say the model's limits come into view. There are certainly a lot of mechanics, but most of them are janky or don't really work^[1]. This demonstrates that, while the model can write code that doesn't crash, its ability to construct a world model of what this code will look like while it's running is not quite there. Moreover, there seem to be abortive hints at mechanics that never got finished, like the named 'POIs' in the fishing game, and it feels like each attempt at implementing a feature ignored all of the other features^[2]. 

It is worth noting that there was some manual - though limited - intervention in generating these games, though none of the code or ideas beyond the starting prompt was human. Nonetheless, it does a good job of showing the failure modes of frontier LLMs on projects for which there isn't a template or an easy way of checking their work.

1. ^{^}

   For example, the powerups and collision mechanics in the racing game rarely do anything, the physics feel off in a dozen odd ways, and plenty of things are rendered in a manner that makes no sense.

2. ^{^}

   For example, the fishing game features solid 'blocks' in the water, which are drawn as if they're supposed to be the sea floor, with one bumpy side and three flat sides. The LLM later decided it was going to add caves, so every 'wall' of the cave uses the same sprite, and at no point did the model consider that its earlier work needed to be updated.