Theo compares Opus 5.5, GPT 6.1 Sol and Muse Wonder Full on one bug fix and says he likes how Muse pointed to an existing fix
Asked Opus 5.5, GPT-6.1 Sol, and Muse Wonder Full to fix a bug Opus found the root cause and filed a pr GPT-6.1 Sol asked a question, crashed from the async question tool interrupt, found the bug, fixed it, wrote 300 lines of tests, and explained it in impossible to understand terms. Muse Wonder Full found a small PR someone else had filed and said "you should probably merge this instead" I really like this model.
Context
Developers with large followings shape which coding models others try, and this anecdote highlights differences in how the top models approach the same task.
Theo Browne runs the t3.gg developer channels and is an influential voice on AI coding tools. This is a single informal test, not a benchmark. Muse Wonder, the third model, was already covered on Oct 12.
Deep dive
On October 13, Theo posted that he asked Opus 5.5, GPT 6.1 Sol and Muse Wonder Full to fix a bug.
According to his post, Opus found the root cause and filed a pull request. He wrote that GPT 6.1 Sol asked a question, crashed from the async question tool interrupt, then found the bug, fixed it, wrote 300 lines of tests and explained it in terms he called impossible to understand.
He wrote that Muse Wonder Full found a small pull request someone else had already filed and suggested merging that instead, and he added: I really like this model.
The post describes one bug in one codebase and does not share the repository, prompts or harness, so it cannot be reproduced. It still reflects a theme in developer discussion this month: the newest models differ less in raw ability than in judgment, such as whether a model writes new code or recognizes that a fix already exists. Theo did not say which harness or settings he used for each model, which can change results a lot.
What to expect next
Watch whether other developers report similar behavior from Muse Wonder Full, especially its tendency to reuse existing work rather than write new code.
Also watch for benchmarks that measure judgment in real repositories, such as avoiding duplicate pull requests, since anecdotes like this one are increasingly how developers choose between models. If more developers share side by side tests like this, they could influence which model becomes the default in popular coding tools in the coming weeks.