Unless we do our own benchmarks, we have to take all the marketing fluff from the frontier labs at face value, and all public benchmarks degrade eventually as labs optimize towards them. OP’s approach is wasteful because it is brute force, but post says that an ELO is kept, so this is also an experiment, and I don‘t see what‘s wrong with that. You learn which model performs well in which settings which may save resources later. It‘s also wasteful to keep working with the wrong model/harness/tools for too long.

It is the other way round.

In an interactive session, adding "Fine, but make the button red" after the model generated a first solution more than doubles the tokens used. As the model now not only gets the original code and the feature request but also the updated code plus the change request as input tokens.

Sending a feature request to an LLM and then sending the feature request again with "The button shall be red" only doubles the tokens used.

Probably like 1% of the energy an average person spends on driving.

The cost is nothing compared to the outcome and time savings. What I see is that people with no money want to jump into this pool but they aren't having a good time. That is generally the case when you are poor.

come on now, we can't just not escape the permanent underclass by using our brains, we've also got to use up all the resources while doing it.

From the SOTA model providers, I only use OpenAI and Google. And between gpt-5.5 and gemini-3.1-pro-preview, gpt-5.5 is currently leading.