Choosing an AI model for ad creative
A way to pick an image or video model that still works after the next release.
I read 874 lines naming an AI video model across 114 threads from marketers and creators. For every line asking which model should I use? there were thirty-three announcing a new one. Almost everything written about models is release news. Almost none of it helps you decide.
The questions people do ask mostly go unanswered:
Which is the best AI video generation model?
Seedance 2.5 vs Seedance 2.0 for AI UGC — who got it better?
"Which is best" has no stable answer, because the field moves faster than any answer lasts. One model line went through several versions inside a single month of these threads. So this guide doesn't rank models. It gives you the axes that matter and a test you can re-run.
The four axes that actually differ
| Axis | What it means | Matters for ads? |
|---|---|---|
| Fidelity per frame | How good a single still looks | Least. Nearly every current model clears the bar, and it's all launch reels show you |
| Consistency across a set | Same face, product and room across forty clips | Most. It decides whether you can run a campaign |
| Physics under motion | Fast movement, hands, objects interacting | Situational. Vital for product demos, irrelevant for talking heads |
| Cost per usable output | Price divided by the share you'd actually run | Most. A cheaper model with twice the discard rate isn't cheaper |
For ad work, choose on consistency and cost per usable output. That's the opposite of how launch posts are ordered.
The switching cost nobody mentions
If your ads depend on a recurring face, the model isn't just a tool. Your ads depend on it. Switch models and the face changes, and any recognition you'd built resets. That recognition is the only part of this that compounds.
- Stay current: always use the newest model and accept that the look drifts. Right for volume testing, where every ad stands alone.
- Pin and hold: lock one model for the life of a persona. Right for any face people are meant to recognise.
Most teams are in the second position but behave as if they're in the first, upgrading on release day.
A test that survives the next release
- Write down which axis limits you, in one sentence.
- Keep three fixed prompts in a file: one talking to camera, one product interaction, one that must match a face from an earlier clip.
- Generate ten of each per model and count the ones you'd actually run. That discard rate is the number that maps to money.
- Decide your posture (current or pinned) before you test, because it changes which result matters.
- Re-run the test when something ships. Don't switch on the announcement.
Current models worth putting through it
As of October 2026. Expect this list to go out of date; the test won't.
| Images | Video |
|---|---|
| Nano Banana 2, Nano Banana Pro | Veo 3.1, Veo 3.1 Fast |
| GPT Image 2 | Seedance 2.0, 2.0 Mini, 2.5 |
| Seedream 4.5 | Kling 3.0 Standard, Kling 3.0 Pro |
| Flux.2 Klein, Pro, Max | Hailuo 3 |
Running the same three prompts across this many models is easiest when they all sit behind one login. I built Clear Cortex, an AI image and video studio for marketing teams, partly for that: every model above in one place, with results landing in one library so you can compare discard rates side by side.