There is no universal answer, and anyone offering one is selling something. Here it is by task type.
Long context and repository work
Claude has traditionally been more comfortable here. It holds relationships between files more steadily and loses the thread less often on long tasks.
If your typical job is "work out how this project fits together and change six places coherently", the difference shows.
Following a format
When you need strictly shaped output, JSON against a schema or a rigid template, the models behave differently. GPT tends to hit the format first time; Claude leans toward adding an explanation unless explicitly told not to.
Fixable with a line in the prompt, but worth knowing.
Agentic work
The ecosystem around Claude Code grew fast, and many tools were written against the Anthropic API specifically. That is a property of the market rather than the model, but in practice it means less setup friction.
Speed
Comparison here is meaningless outside a specific version: both sides ship fast variants and the ranking shifts every few months. Measure on your own task rather than someone else's benchmark.
Cost per iteration
More important than price per token is how many attempts a task takes. A model twenty percent more expensive that lands first time instead of third is cheaper overall.
So compare cost per solved task, not the rate card. That is the only metric that means anything.
The practical conclusion
Do not pick one forever. Keep both and split by task:
- code comprehension and large refactors: Claude
- strict structured output and bulk processing: often GPT
- mechanics and simple transformations: the smaller model in either family
Keeping both without friction
Through one endpoint that speaks both formats. We serve Claude and GPT models alike, and switching is a change of identifier in the request. One key, one balance, one set of stats.
That turns the comparison from theoretical into practical: run your real task on both and look at the cost of the result.