When a team hits a ceiling with their coding agent, the first instinct is to reach for a better model. The reasoning feels obvious: the model is the part that produces the code, the code is the part that is wrong, therefore a smarter model will produce more correct code. Wait for the next release. Switch providers. Bump the tier.
This is sometimes right. It is much more often wrong, and the cost of being wrong about it is that you spend months waiting for a model upgrade to solve a problem the model was never the cause of.
The harness is the cause of the problem more often than the model. Most teams discover this only after they have exhausted the model-upgrade reflex and finally turn to look at everything else.
What a model upgrade actually buys you
Newer, stronger models do tangibly improve some things. They handle longer contexts more reliably. They make fewer simple reasoning errors on complex tasks. They follow nuanced instructions more closely. On a fixed prompt, with a fixed task, a better model produces a better answer.
