There is no universal answer, and anyone who offers one is selling something. So we go by task type rather than by general impression.
Long context and repository work
Claude has traditionally felt more at home here. It holds the links between files more steadily and loses the thread less often when a task runs long.
If your usual job sounds like "work out how this project fits together and change six places coherently", the difference shows right away.
Following an output format
When the output has to take a strict shape, whether JSON against a schema or a rigid template, the models behave differently. GPT tends to hit the format on the first attempt. Claude leans toward adding an explanation unless you explicitly tell it not to.
A line in the prompt fixes it, but it helps to know in advance.
Agentic work
The ecosystem around Claude Code grew fast, and many tools were written against the Anthropic API from the start. That is a property of the market, not of the model. In practice it means less setup friction.
Speed
A speed comparison means little without a specific version. Both sides ship fast model variants, and the ranking shifts every few months. Measure on your own task, not on someone else's benchmark.
Cost per iteration
The price per token matters less than the number of attempts a task takes. A model that costs 20% more but lands on the first try instead of the third ends up cheaper.
So compare the cost of a solved task, not the rate card. No other metric means anything here.
What to pick in practice
Do not choose one model forever. Keep both and split the work by task:
- code comprehension and large refactors: Claude
- strict structured output and bulk processing: often GPT
- mechanical work and simple transformations: the smaller model of either family
Keeping both without the hassle
Use one endpoint that speaks both formats. codexkey serves Claude and GPT models alike, and switching comes down to changing the identifier in the request. One key, one balance, one set of stats.
That turns the comparison from theory into practice: run your real task on both models and look at what the result costs.