Not a benchmark — five real tasks, side by side: doing the work, holding a brand voice, handling a big job, and where each one actually fits.
The first task wasn't 'explain how' — it was 'do it.' ChatGPT returned a clear set of steps; Claude (Code) edited the files directly and ran the tests. For hands-on work, an agent that acts beats one that advises.
If you find yourself copying an AI's instructions and doing the work yourself, that's the gap this closes.
Given the same brand rules, Claude held the voice consistently across multiple outputs, while generic output tends to drift. For anyone publishing regularly, consistency is the whole game.
On a multi-file refactor, Claude planned the work and ran subagents in parallel, then verified — the kind of breadth a single chat can't hold. This is where agentic tools pull clearly ahead.
Small, single-answer questions don't need this; large, many-step jobs do.
For fast one-off questions and brainstorming, ChatGPT is excellent and often quicker to reach. This isn't tribal — it's task-fit. The point isn't 'X is bad'; it's match the tool to the job.
For real, hands-on work that has to ship, though, the agent that does the work wins.
The full side-by-side: the five tasks, what each tool did, and a simple 'which do I grab when' guide.