Claude vs ChatGPT for Marketing: 12 Tasks Scored

Neither one wins. Across 12 named marketing tasks scored on a single published rubric, Claude Opus 5 took 5, GPT-5.6 Sol took 4, and 3 were ties. Claude won everything involving voice, long documents and code. ChatGPT won everything involving volume, rigid formats and running arithmetic over a file. If you can only pay for one, the deciding question is not "which model is smarter" but "which of those two piles is most of my week."

Below is the rubric, the scoreboard, what one full pass cost in tokens, and a script so you can rerun it on whatever models exist when you read this.

The rubric, stated before the scores

One axis, because a single honest axis beats four invented ones: how much editing stood between the first output and something I would actually publish or hand a client.

Method, in full, so you can discount it properly: one run per task per model, one scorer (me), same prompt text sent to both, no system prompt beyond a two-line role, tested 4-5 September 2026 on Claude Opus 5 and GPT-5.6 Sol via API. This is not a statistical benchmark. It is twelve controlled comparisons by one person, and I am telling you that up front because the useful output here is the task list and the method, not my numbers.

Model behaviour changes. Anthropic and OpenAI both shipped model updates in the months before I wrote this, and either could invert a row below without warning. Treat the scoreboard as a snapshot and the rubric as the durable part.

The scoreboard

Token figures are the envelope I budgeted each task at, not a measurement of my run. They are assumptions, priced at published list rates: Claude Opus 5 at $5 / $25 per million input / output tokens (Anthropic pricing docs) and GPT-5.6 Sol at $4 / $20 (OpenAI pricing docs), both checked 5 September 2026. Sol's rate is promotional at least through 21 November 2026, so the gap widens when it ends.

One full pass of all twelve, at those envelopes: $1.42 on Opus 5, $1.13 on Sol. Opus costs 25% more, a gap that is irrelevant next to the cost of your editing time, which is the actual finding of the whole exercise.

Where Claude won, and why

Tasks 6, 7 and 9 are the same task wearing three hats: hold a long constraint in your head and do not drift from it. Given a style guide and 900 words, Opus 5 cut filler and left my sentence rhythm alone. Sol rewrote me into a cleaner, flatter register that was not mine. On the 90-page document, Sol answered from the first third and missed a positioning line on page 61; Opus 5 quoted it.

Task 8 surprised me least. Most of what I have built this year — four platforms in 25 days — came out of a Claude Code loop, so I already knew where that one was going.

Where ChatGPT won, and why

Tasks 3, 10 and 12 reward volume and obedience to a hard constraint. Asked for ten titles at 60 characters or fewer, Sol counted correctly and gave me ten. Opus 5 gave me eight in range and two at 63 and 66. That is a small failure that costs real minutes at scale.

Task 4 is the one that changed my mind. I used to call data interpretation a tie. It is not, when the tool actually executes code: Sol wrote Python, ran it over the CSV, and reported computed numbers. Opus 5 without an execution tool reasoned over a truncated read of the file and produced a shape that looked right. Looking right is exactly the failure mode you cannot afford in analytics.

Where both were mediocre

Task 2, keyword clustering, scored 3 on both, and it deserves the low mark. Both models grouped queries by surface word overlap. Neither knew search volume, neither knew which cluster my domain could plausibly rank for, and both invented tidy category names that flattered the list. Clustering without volume and difficulty data is vocabulary sorting, not keyword research. Neither model can fix that, because the missing thing is data, not intelligence.

Run it yourself

The scoreboard ages. The costing does not. This is stdlib Python, no keys required — it prices a task envelope across whichever models you list, so you can sanity-check a workflow before you build it.

"""Cost one marketing task across models, from published list prices.
Run: python bench_cost.py
Prices in USD per million tokens, from vendor docs, checked 2026-09-05.
Re-check them before you trust the output: they move.
"""

PRICES = {  # model: (input $/Mtok, output $/Mtok)
    "claude-opus-5":    (5.00, 25.00),
    "claude-sonnet-5":  (2.00, 10.00),
    "claude-haiku-4-5": (1.00,  5.00),
    "gpt-5.6-sol":      (4.00, 20.00),
    "gpt-5.6-terra":    (2.00, 12.00),
    "gpt-5.6-luna":     (0.20,  1.20),
}

TASKS = [  # (name, input tokens, output tokens) - budgeted envelopes, not measurements
    ("content brief",        2_000,  1_200),
    ("keyword clustering",   6_000,  2_000),
    ("20 ad variants",         800,    900),
    ("GSC csv analysis",    45_000,  1_200),
    ("json-ld schema",       1_500,    800),
    ("style-guide edit",     4_000,  1_800),
    ("90-page doc QA",     120_000,    900),
    ("python script",        1_200,  1_600),
    ("voice-match rewrite",  5_000,  1_500),
    ("titles + metas",         600,    400),
    ("competitor teardown", 15_000,  1_400),
    ("repurpose to social",  3_000,  2_200),
]

def cost(model, tok_in, tok_out):
    p_in, p_out = PRICES[model]
    return (tok_in * p_in + tok_out * p_out) / 1_000_000

def report(models):
    totals = dict.fromkeys(models, 0.0)
    print(f"{'task':<22}" + "".join(f"{m:>18}" for m in models))
    for name, tok_in, tok_out in TASKS:
        cells = ""
        for m in models:
            c = cost(m, tok_in, tok_out)
            totals[m] += c
            cells += f"{'$' + format(c, '.3f'):>18}"
        print(f"{name:<22}{cells}")
    print(f"{'TOTAL':<22}" + "".join(f"{'$' + format(totals[m], '.2f'):>18}" for m in models))

if __name__ == "__main__":
    assert round(cost("claude-opus-5", 1_000_000, 0), 2) == 5.00
    assert round(cost("gpt-5.6-sol", 45_000, 1_200), 3) == 0.204
    report(["claude-opus-5", "gpt-5.6-sol"])

Swap in a cheaper model and the totals collapse. That is the point of the next section.

The question nobody asks: which tier, not which brand

Nine of the twelve tasks above do not need a frontier model at all. Schema generation, meta descriptions, ad variants, repurposing — these are format transformations with a checkable output. Priced at the same envelopes, running the full twelve on Claude Haiku 4.5 ($1 / $5 per million) costs about a fifth of the Opus figure.

I went through this properly when I benchmarked open-weight models for a pipeline: how I pick a model by task, not by leaderboard. The rule that survived is a two-tier split — a cheap fast model for anything with a verifiable output, an expensive one only where judgement is the product. Brand voice is judgement. Character counting is not.

Prompt quality closes more of the gap than model choice does, which is why the five prompts I actually reuse moved my output more than any upgrade.

Why the comparison matters less than it used to

Whichever model you pick, its output lands in a search environment that is eating the clicks. Pew Research Center analysed 68,879 Google searches from 900 US adults during March 2025 and found users clicked a traditional result on 8% of visits when an AI summary appeared, against 15% when none did — and clicked a link inside the summary itself on just 1% of visits (Pew Research Center, July 2025). Sessions ended outright on 26% of pages carrying a summary, against 16% without.

My own Search Console agrees about what survives. Over the 85 days to 3 September 2026 this site took 4,553 impressions and 22 clicks — a 0.48% CTR. Split it by content type and the pattern is blunt: posts documenting things I built with Claude Code, Supabase and Vercel sit at positions 2.8 to 15.5. Posts of generic "[industry] content marketing" advice sit at 46 to 81. Same author, same domain, same quarter. This post was one of the 81s.

Neither model produces the first kind on its own. A model can only reformat what you give it, and generic input is what makes generic output. More on that split in AI SEO vs traditional SEO and in structuring a page so it gets extracted.

FAQ

Is Claude or ChatGPT better for marketing?

Neither, across the board. In my 12-task run Claude won voice matching, style-guide editing, long-document QA and code; ChatGPT won high-volume variant generation, hard character limits and executing analysis over a file. Pick by the tasks that fill your week.

Which is better, ChatGPT or Claude for content creation?

Claude, if content creation means drafting and editing in a defined voice — it scored 5 on both the style-guide edit and the voice-match rewrite where ChatGPT scored 3. ChatGPT, if it means producing 20 variants of a short asset quickly.

Claude vs ChatGPT for market research — which one?

ChatGPT, when the tool can run code over your data: it computed answers from a CSV where Claude without an execution tool reasoned over a partial read and produced a plausible-looking shape. For synthesising a long PDF or brand document into positioning, Claude held the far end of a 90-page file better.

Do I need to pay for both?

Only if both piles are large. Test it cheaply first: run five tasks you genuinely repeat through each, score them on the 1-5 edit-distance rubric above, and let the numbers decide. That costs a couple of dollars in API credit and beats a year of subscribing to both out of anxiety.

Not sure which of your marketing tasks should go to a model at all? I will map your workflow, score the candidates on your own tasks, and hand you the routing rules. See my services or get in touch.