Which AI Model Should Write Your Marketing and Sales Content?

Swap Claude Opus 5.5 for GPT-6 Luna on a 1,500-word article and you save about 16 cents in tokens. An editor paid US$60 an hour burns through that in under ten seconds. That is an illustrative workload at list prices on 6 October 2026, and the arithmetic is below. It is also the whole argument of this guide: no single AI model is best for marketing and sales content, so route each task to the model that needs the fewest human editing minutes.

September 2026 reset the menu. Claude Opus 5.5, Claude Sonnet 5.5, GPT-6.1 Sol and GPT-6 Luna all launched, and Gemini 4 Argon was announced for a small partner group. At OpenAI alone, standard-tier list output prices now run from US$0.50 per million tokens for GPT-6 Luna to US$50 for GPT-6 Astra, a 100-fold gap, according to OpenAI's own pricing page on 6 October 2026. For anything a person reads before it ships, neither number sets your bill. Editing minutes do. This guide is for marketing leads and founders who own a content budget and want a routing rule they can defend.

What changed in September 2026, and what does each model cost?

Six models that score above 50 on the Artificial Analysis index were released or announced in September 2026, and three of them share one price point. Claude Sonnet 5.5, GPT-6.1 Sol and the introductory price of Gemini 4 Argon all sit at US$2 per million input tokens and US$10 per million output tokens. Price no longer separates the mid-to-top tier. Quality on your own copy does.

Check the Access column before you plan around a model: one of these is partners-only.

Sources: Anthropic's pricing page, OpenAI's developer changelog, Google's Gemini 4 Argon announcement, Google's Gemini 3.8 Flash post and DeepSeek's pricing page, all read on 6 October 2026.

The chart carries the first surprise. On the Artificial Analysis leaderboard (Intelligence Index v4.3, fetched 6 October 2026), Claude Opus 5.5 scores 57.6 and Sonnet 5.5 scores 56.0 at half the price. Above US$10 per million output tokens, extra money buys very little extra score. GPT-6 Astra and Claude Fable 5.1 cost five times Sonnet 5.5 and score lower. Two caveats keep this honest. Each score is the model's best effort setting, which burns more tokens than the default. And the index measures reasoning and knowledge work. It never reads a line of prose.

Do leaderboards tell you which model writes best?

No public leaderboard measures marketing copy in your brand voice, and the two that come closest rank the same models in different orders. The Artificial Analysis index scores reasoning, agents and knowledge work. The arena.ai Creative Writing board (2 October 2026) ranks models by anonymous head-to-head votes on prose. Put eight models side by side and the order moves.

Claude Sonnet 5.5 is second of the eight on the index and seventh on creative-writing votes (only 658 votes, so treat the exact rank loosely). Gemini 3.8 Flash goes the other way, from seventh to third, at less than 40% of Sonnet's output price until its price doubles on 1 January. At the top, Gemini 4 Argon (1,519) and Claude Opus 5.5 (1,516) are a statistical tie. arena.ai marks the Argon and Gemini 3.8 Flash scores as preliminary, and Argon is not yet on general sale.

What does that mean for a content lead? Treat any leaderboard as a shortlist. Neither board has read your brand guide. If the boards cannot tell you which model writes best for you, something cheaper has to. It turns out to be a stopwatch.

Why is the token bill the wrong number to optimise?

For anything a person reads before it ships, the editor costs hundreds of times more than the model. Here is the arithmetic. Anthropic's pricing page says one token is about 0.75 English words. A 1,500-word article is roughly 2,000 tokens, or about 2,600 on newer Claude models, which Anthropic says produce "approximately 30% more tokens for the same text".

Illustrative example: a 1,500-word article, drafted twice, with 8,000 input tokens (brief, sources, brand rules) and 2,600 output tokens per draft. On Claude Opus 5.5 the tokens cost US$0.17. On GPT-6 Luna they cost US$0.004. The 45 minutes your editor spends on it at US$60 an hour cost US$45. Switching from Opus 5.5 to Luna saves 16 cents: under ten seconds of that editor's time. If the cheaper draft needs one extra minute of fixing, it is the dearer choice. Reasoning modes add more output tokens, because Anthropic bills thinking as output: 20,000 thinking tokens per draft would lift the Opus 5.5 bill to about US$0.97 an article, still less than one minute of editing.

That arithmetic flips for work nobody reads in full. Ten thousand personalised email openers at 2,000 input and 300 output tokens each cost about US$140 on Opus 5.5 and about US$3.50 on GPT-6 Luna. A person still reviews a 1-in-20 sample, which is 500 messages or roughly US$500 of editor time at a minute each, whichever model wrote them. Here the model choice moves about US$136, more than two hours of editor time, and the cheap model earns its place. So the right model depends less on the task than on who reads the output. That is the whole routing rule.

Which model should handle which marketing and sales task?

Route by who reads the output. If a person approves every piece, use the model that needs the fewest edits; if a person checks a sample, use the cheapest model that passes your test. Escalate on clear triggers. The matrix applies that rule to eight common jobs.

Two rows deserve a warning. Sales prompts often carry names, deal values or notes from your CRM, so check the data terms before you pick a tier. Anthropic, for one, says Sonnet 5.5 "is available with zero data retention"; check whether your plan includes it. And no tier removes the need for a person to sign off price, performance or regulated claims, which is where the liability sits. Our post on AI ad copy claims covers that check.

Not sure which row your job is? Four questions below.

How do you test two models on your own brand voice in 20 minutes?

Give both models the same three approved examples, the same facts and the same task, then score the drafts and time the edits. Twenty minutes of this beats any leaderboard, because it measures the one number the calculator needs: editing minutes on your own copy. Run it again whenever a vendor ships a new version.

What if your model is withdrawn or repriced?

Plan for change, because in one month a planned release was reportedly shelved, a price was scheduled to double and a vendor retired its older models. On 28 September 2026, 9to5Google reported, citing The New York Times, that OpenAI had scrapped plans to release GPT-6.1 Astra in the coming weeks after safety testing. Google's Gemini 3.8 Flash price is scheduled to double on 1 January 2027. DeepSeek retired its V4 Flash models on 10 September, and its change log shows it announced, then reversed, a plan to route V4 Pro requests to V4.1 Flash.

None of that should break a content operation. Open-weight models add an option, with strings: "open weights" is not the same as open source, and licences such as Kimi K3's add conditions for large commercial users. Three habits keep you portable. Keep brand rules, approved examples and fact sheets in your own documents, where any model can read them. Write prompts in plain text that travels between vendors. And log which model and version produced each published piece, so a quality drop can be traced to a model change rather than guessed at.

Where Origin Pi stands

So which model should write your content? The one your editor touches least, on your copy, this month. September made strong models cheap enough that 16 cents of tokens will never be the deciding number for an article a person reads. Ten minutes of extra editing will be. We treat model choice for content as an operations decision rather than a procurement one, and that is what we build: a routing layer that sends each content task to the right tier, a confirm step before any price or result claim goes live, and a log of model, version and approver for every published asset. Our post on AI agent cost per completed task applies the same arithmetic to agents, and our marketing AI and business automation services cover the build. For campaign execution, the card below links to Origin Pi's marketing consultancy brand leapbuzz.

Your first step: take one asset type you publish every week, run the brand-voice test on your current model and one cheaper model, and time the edits. If the cheaper draft costs more than a minute of extra editing, keep the model you have.