"Which model is better for marketing copy" is the wrong question, and the confident answers you'll find are usually one person's taste generalised into a verdict. The useful question is narrower: which model is better for your brief, your brand voice, and the specific job you keep repeating?
This article gives you a method for answering that in about twenty minutes, plus the differences that tend to show up when people run it.
Why published comparisons rarely help you
Three reasons, all of them structural:
- Model versions move. Any comparison is a snapshot. Providers ship updates that change tone and instruction-following, and a verdict from six months ago may describe a model that no longer exists.
- "Marketing copy" isn't one task. A 40-character subject line, a 2,000-word landing page and 20 Instagram captions reward completely different things. A model that wins one can lose another.
- Brand voice dominates. Once you've written a strong voice prompt, the gap between good models narrows sharply. Most of the quality difference people attribute to the model actually comes from the brief.
That last point is worth sitting with. If you're choosing between models before you've written a proper voice prompt, you're optimising the wrong variable.
The 20-minute comparison
The method is a controlled test: same prompt, same variables, different model, then judge blind.
1. Pick one job you actually repeat. Not "write marketing copy" — something like "5 Instagram captions for a product launch" or "3 subject lines for a re-engagement email". A real recurring task.
2. Write one prompt, properly. Include the voice constraints, the format, and the negatives:
You are a copywriter for {{brand}}. Voice: {{brand_voice}}.
Write {{count}} {{format}} for {{product}}, aimed at {{audience}}.
Hard constraints: under {{max_length}} characters each. No exclamation
marks. Never use "elevate", "unlock", "game-changing", "seamless".
Return a numbered list and nothing else.
3. Run it on both models, unchanged. Same variable values, same everything. This is the part people get wrong — if you tweak the prompt between runs, you're comparing prompts, not models.
4. Run it three times per model. These systems are non-deterministic. A single run tells you about one sample, not about the model.
5. Judge blind. Strip the labels, shuffle the outputs, and score against criteria you set before looking:
- Does it obey the hard constraints (length, banned words, format)?
- Does it sound like the brand, or like generic AI marketing?
- How many of the options are actually usable without editing?
- How much editing would the best one need?
That last metric — usable options per run — is the one that predicts real-world value. A model producing two great captions and three unusable ones beats one producing five mediocre ones, because you only need one.
What tends to differ
With the caveat that this varies by version and by brief, these are the differences people most consistently report when they run the test above.
Instruction adherence under constraint. When a prompt stacks several hard rules — a character limit and banned words and a strict output format — models differ in which rule they drop first. This is the most practically important difference, and it's the easiest to test: count the violations.
Default register. Every model has a house style it reverts to when the brief is vague. Some default to more effusive, adjective-heavy marketing language; others to something flatter and more literal. Neither is better in the abstract — but one will be closer to your brand, which means less correction.
Behaviour at length. Short-form and long-form reward different things. A model that's excellent at punchy captions may over-structure a landing page with unnecessary headers, and vice versa. Test the length you actually need.
Response to negative constraints. Telling a model what not to do is the most effective brand-voice lever, and models vary in how reliably they honour a banned-word list across several generations.
Rather than trusting any of that, measure it. The scoring rubric above turns all four into numbers in about twenty minutes.
Don't pick one — that's the actual answer
The most common outcome of running this properly is that different models win different jobs on the same account. Short social copy from one, long-form from another, subject lines from a third.
Which means the practical answer isn't "pick the best model". It's keep the same prompt and switch the model per job. That requires two things:
- Prompts stored independently of any one provider's interface, so the same text can run anywhere.
- Your own API keys for each provider, so switching costs nothing and you pay the provider directly rather than a markup.
This is exactly what a prompt library is for. The prompt is the asset; the model is a runtime choice you should be able to change without rewriting anything.
Re-run it quarterly
Models update. The comparison you ran in January may not describe the same systems in June, and providers rarely announce tone changes.
Keep the test prompt saved in your library. Once a quarter, re-run it on the current models and re-score blind. It takes twenty minutes and it stops you carrying a stale verdict for a year — which, given how fast this moves, is the most expensive mistake available.