Most answers to this question come from someone selling one of them. We train professionals on both, every week, and the honest answer hasn't changed in two years: not one of them is better than the others at everything. Choose the right tool for the job.
Our standing advice: keep two or three in your armoury. Major on one - but have the others as backup, because each can deliver a great result and none is best at everything. The choice of which to major on follows your tasks and goals, not benchmark headlines. And whichever you pick: the practice you build matters more than the platform you choose.
For a long stretch, the rooms we trained were besotted with ChatGPT - many hadn't even tried Claude. And our own early preference ran the same way, for a specific, practical reason: custom instructions let us build the foundation - the individual persona at the heart of the Darla Method - in greater detail, and memory arrived earlier, so the persona took hold more strongly. That mattered more than any benchmark.
Then the picture moved. In session after session we watched participants struggle to get their heads around Custom GPTs - while Skills, Claude's equivalent idea, clicked straight away. The expansion of Skills, plugins and the wider connected toolkit has made the integration story for teams and enterprises genuinely hard to look past. And the old gap has closed: Claude now holds a persona, and remembers who it's working with and how, as well as anything we train on.
One thing we deliberately leave out of this comparison: the world of people spending hours in coding tools. That's a small fraction of everyone who will ever use AI, and it isn't who we serve. The people in our rooms aren't looking to become AI experts - they already have a job. They want to be better at it.
Clients ask us constantly which to standardise on, and we won't hand over a template verdict, because the answer has to be individual. What we've watched companies actually do: trial both for a period, watch which one their people reach for, and let the tasks and goals of the individuals doing the work drive the decision. Alongside that sit the practical deciders - your existing stack and IT estate, your data posture and what your regulators require (a Diligence question, and yours to own), and who has to adopt it, because the best platform is the one your people will actually use daily.
What we can do is what we do in every session: lay out the current capabilities of each honestly, show where each is stronger, and equip you to make the call yourself - this quarter and again next quarter, because the capabilities won't sit still.
We hear it in almost every first conversation - and we already know what we'll find: barely the surface scraped. There's an understandable assumption among intelligent people that they know what they're doing with it, when in truth they haven't invested a moment beyond flicking the switch and trying to get something out - without ever putting anything in first so it could perform better.
That's not a criticism; it's the most natural mistake in the world, and the most expensive one. These tools perform in proportion to what you invest at the start - which is exactly why the Darla Method begins by building the foundation, and why "already using it" and "getting real value from it" are usually two different statements.
All of the leading platforms are very good now - there's no doubting that. What ultimately defines the difference is how you adapt them to your work. So major on one, keep the others in the armoury, and put your real effort where the return actually lives: the foundation you build, the practice you keep, and the craft of working with whichever tool the job in front of you calls for.
Related reading: Why most AI training fails · AI fluency vs prompt engineering · The Small Business Programme