"Which is best" is the question everybody asks and the one with no stable answer.
Models change every few months, and a ranking written today is stale by spring. What does not change is the criterion you choose by, and that is the part worth learning.
The criterion that does not age
Do not pick the best model. Pick the one already living where you work.
The time lost copying text between a browser tab and your document exceeds any quality gap between two serious models. When your work lives in Google Docs, a model integrated there saves you more than a slightly better model elsewhere earns you.
How they differ, on real tasks
| Task | What actually matters | How to check |
|---|---|---|
| Long coherent writing | holding the thread for 1,500 words | have it write, then read the ending |
| Research with sources | citing real pages, not plausible ones | open three links at random |
| Data analysis | not inventing absent numbers | hand it a file with a gap |
| Volume drafting | cost per output, not peak quality | 20 variants, count the rejects |
| Team work | integration and permissions | how long a colleague takes |
The column that counts is the third. Differences between models show up by trying them on your own material, not by reading comparisons: ten minutes on your case is worth more than ten articles like this one.
The test we run before adopting one
- NOIHand it an old piece of yours and ask for criticism — see whether it grasps context or repeats generalities
- NOIAsk for a figure from your sector with the source — then open it. This eliminates several
- AIHave it write 20 ad variants — count how many you would discard
- NOIGive it an incomplete brief — a useful model asks, a useless one invents
- NOILet a non-technical colleague use it — needing a course means nobody at the agency will
The fourth is the most informative of all, and almost nobody runs it. A model that confidently produces something anyway, from a brief full of holes, is more dangerous than one that stops and asks.
How many you actually need
One good one, used well, beats three used badly.
The temptation to subscribe to all of them is strong and expensive: €20-30 a month each, and multiplied across a year it becomes a real line. Paying for more than one makes sense only with a recurring task where one is clearly better, verified by you rather than read somewhere.
What almost always pays off instead is keeping the ability to switch: do not build the whole workflow around one model's quirks, because in six months the ranking will differ. The tools we use and what they cost are in the stack.
What does not change, whichever you choose
Three things hold across all of them, and they decide whether the work comes out well.
The brief matters more than the model. A brief with context, audience, constraints and examples produces better output on any model than a thin brief does on the best one.
Numbers always get checked. Every model invents statistics with exactly the face it wears when reporting real ones. On a client site, one invented number is damage you cannot undo.
Final judgement stays human. Deciding a draft is publishable is an editorial call, and delegating it is exactly how AI slop gets made. What you can genuinely hand an agent is in the AI agents guide.
Frequently asked questions
What is the best AI for marketing?
There is no stable answer, because models change every few months and a ranking written today ages within a season. The criterion that does not age is different: pick the one integrated where you already work, because time lost copying text between tabs exceeds any quality gap between two serious models.
Is it worth subscribing to more than one AI model?
Rarely. They run €20-30 a month each and across a year that becomes a real line item. Paying for more than one makes sense only when you have a recurring task where one is clearly better, and you verified that on your own material rather than reading it in a comparison.
How do you test a model before adopting it?
With five tests on your own material: have it criticise an old piece of yours, ask for a figure from your sector with the source and then open it, have it write twenty variants and count the rejects, give it an incomplete brief, and let a non-technical colleague use it. The fourth is the most informative.
Why give a model an incomplete brief during testing?
Because it separates useful models from dangerous ones. A useful one stops and asks what is missing; one that confidently produces something anyway is filling gaps with plausible invention, and on real work that invention travels all the way to the client.
Can AI write the content for me?
It can write drafts, and quickly. It cannot decide a draft is publishable, which is an editorial judgement about a specific audience at a specific moment. When that decision gets delegated it shows, and that is exactly how AI slop gets made.
What matters more than which model you pick?
The brief. With context, audience, constraints and examples, any serious model produces better output than the best model does with a thin brief. Right behind it comes checking the numbers, because every model invents statistics with the same confidence it reports real ones.
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Founder of As Growth, an AI-first growth partner. He runs growth and strategy across partner businesses: OroEtic, Giojello, Equity Plus and others. Apply if you want to work with us.