Every AI UGC video starts life as a still image — the persona portrait, the product composite, the scene frame your video engine animates. UGC Copilot now gives you a choice of two image engines for that job: the default Gemini stack (the "Nano Banana" models) and OpenAI's GPT Image 2. This is the same-prompt, same-workflow comparison — where each engine wins, what each costs in credits, and a per-shot decision guide.
If you've read our 3-way video engine test, the thesis will feel familiar: no single model wins every shot. That's now true for images too.
The 30-second answer
GPT Image 2 wins on product-label fidelity and photorealism; Gemini wins on speed and native vertical framing. If the shot involves packaged goods where the label must read correctly — skincare jars, supplement bottles, snack bags — route it through GPT Image 2. If you're iterating fast on personas and scene drafts, stay on the Gemini default and keep the extra credit.
| GPT Image 2 (OpenAI) | Gemini "Nano Banana" (default) | |
|---|---|---|
| Best at | Product/label accuracy, text in image, hands, photorealism | Speed, fast iteration, native 9:16 vertical |
| Typical speed | 20–60s (up to ~2 min on complex composites) | Seconds |
| Vertical 9:16 | Renders 2:3, auto-cropped to exact 9:16 server-side | Native 9:16 |
| Credits (standard / HQ) | 1 / 3 | 1 / 2 |
| Resolution tiers | Quality tiers only (medium / high) | HQ supports 1K / 2K / 4K output |
What GPT Image 2 actually is
GPT Image 2 (gpt-image-2) is OpenAI's flagship image generation and editing model. Two properties make it unusually good for ad creative:
- It takes your real product photo as an edit reference. The model's editing endpoint accepts multiple reference images in a single request. UGC Copilot passes your uploaded product shot straight into the generation call, so the composite contains your product — shape, colorway, logo, label text — rather than the model's best guess from a text description.
- It renders text and fine detail reliably. Printed labels, embossed logos, and UI screenshots survive generation far more often than on most image models. For DTC brands, that's the difference between a usable composite and an uncanny one.
The trade-offs are real, though. GPT Image 2 is slower — complex composite prompts can take up to two minutes — and it doesn't natively render true 9:16 vertical. It generates square (1:1), portrait (2:3), or landscape (3:2) frames.
How UGC Copilot handles the aspect-ratio gap
This matters more than it sounds. Scene images aren't decorative — engines like Veo 3.1 and Kling 3.0 consume them as the literal first frame of your video, so the aspect ratio has to match your ad format exactly. A 2:3 frame handed to a 9:16 render would stretch or letterbox.
UGC Copilot handles this server-side: GPT Image 2 output is generated at the closest native frame and then center-cropped to your exact requested ratio (a 9:16 request yields a precise 864×1536 frame). Prompts are composed center-weighted, so the crop costs you edge margin, not subject. You never see the intermediate frame — the image that lands in your scene is already video-ready.
Both engines also pass through the same automated quality-control loop before you see a result — an AI inspector checks anatomy (hands, limbs, duplicated people), device orientation, and composition, and auto-retries once on a failure. You're charged once regardless of internal retries.
The credit math
Standard quality costs 1 credit on both engines — switching engines for drafts is free. The difference is at HQ: Gemini HQ is 2 credits, GPT Image 2 HQ is 3. That reflects OpenAI's underlying token-based pricing, where a high-quality image costs roughly 10× a medium-quality one at the API level.
In practice, a typical 4-scene project runs one persona portrait plus four scene images. All-Gemini at HQ: 10 credits. All-GPT at HQ: 15 credits. Mixed — GPT for the persona and the product-reveal scene, Gemini for the rest — lands at 12, which is where most product-ad projects should sit.
Per-shot decision guide
| Shot | Engine | Why |
|---|---|---|
| Persona portrait (hero creator shot) | GPT Image 2 HQ | Best skin realism and hands; this face carries every scene |
| Product composite (creator holding product) | GPT Image 2 HQ | Label/logo fidelity from the direct photo reference |
| Scene drafts / iteration | Gemini standard | Seconds per render; iterate freely at 1 credit |
| Background / faceless b-roll frames | Gemini standard | No faces or labels to protect; speed wins |
| 4K print-adjacent output | Gemini HQ (4K) | Only the Gemini engine exposes 1K/2K/4K resolution tiers |
How to switch engines
On the Create step, next to the Fast/HD quality toggle, you'll find the image engine selector: Gemini or GPT Image. The choice applies to the whole project — persona generation, product composites, every scene image, and scene edits — and the credit label updates live so there are no surprise costs. Quality stays a separate toggle: Fast maps to Gemini Flash or GPT medium; HD maps to Gemini Pro or GPT high.
API users get the same choice: the proxyGenerateImage, proxyGenerateSceneImage, and proxyEditSceneImage endpoints accept an optional imageEngine parameter (gemini | openai), documented in the OpenAPI spec.
The bigger picture: engine choice is the moat
The pattern across 2026 is consistent — the platforms winning at AI creative aren't betting on one lab. UGC Copilot already routes video across five engines (Sora 2, Veo 3.1, Kling 3.0, Seedance 2.0, and Omni); images now work the same way. Models leapfrog each other every quarter. Your workflow shouldn't have to change when they do — you just flip the engine selector and keep shipping.
Ready to test it on your own product? Create a free account, upload a product photo, and generate the same composite on both engines — the label test takes about two minutes and usually settles the question.