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New models arrive faster than a marketing team can test them, so the useful question is rarely which one is best. It is which task you would give each one, what that costs per finished deliverable, and how you would notice if a cheaper model were good enough.

These posts are working notes from using the models on real marketing jobs: briefs, drafts, audits, reporting and agents. Prices and version names are dated in each post, because both go stale within weeks.

104 articles2 guides and pages

Claude and Claude Code 24

Models, effort levels and building with Claude Code.

OpenAI and ChatGPT 27

GPT releases, plans and the Agents API.

Gemini and other models 11

Google, open-weight and on-device models.

Choosing, routing and cost 13

Method posts that outlive any single release.

Agents and automation 22

What agents can safely own in a marketing team.

Questions people ask

Which AI model should a marketing team use?
There is no single answer. Run the same briefs through two candidates, measure human edit time and cost per finished piece, and route accordingly. The framework and test protocol in the reading path describe how.
Do model prices in these posts stay current?
No. Each post states the date its numbers were taken. Treat the method as durable and the figures as a snapshot, and check the vendor pricing page before budgeting.
Are agents ready to run marketing unattended?
For narrow, checkable tasks, partly. For anything public-facing or brand-sensitive, I keep a human review step. The posts on tasks worth automating and on agentic limitations explain where I draw the line.

Related hubs

If you want help designing a routing setup around your actual content operation, say what you produce each week.