How Google's and OpenAI's image models compare for packshots, lifestyle scenes, labels, localized ads and edits, based on what each vendor documents.
Quick answer: Both models can place a real product into new scenes and edit existing photos, so the choice depends on the job. Nano Banana Pro (Google's Gemini 3 Pro Image) suits multi-reference composites and text-heavy creatives. Google documents up to 14 reference images (up to 6 objects at high fidelity, plus character and style references), legible and translatable in-image text, Google Search grounding, and 1K/2K/4K output. GPT Image 2 (OpenAI) suits precise, controlled edits and odd sizes. It processes every input image at high fidelity, supports mask-based inpainting, and accepts custom sizes up to 3840 px on the long edge with ratios up to 3:1. Neither model is perfect with small text, so check every label against the real product.
First, a note on versions
Both vendors ship quickly. As of October 2026, Google's docs list newer Nano Banana 2 and 2.1 models next to Nano Banana Pro, and position Pro as the premium option for complex tasks, localization and brand consistency. OpenAI's image guide now leads with GPT Image 2.5 (Sunburst and Flare) and lists gpt-image-2 under earlier models. This article compares Nano Banana Pro and GPT Image 2 because they're widely available in creative tools. Check which versions your tool offers before you standardize.
What each vendor documents
Nano Banana Pro (gemini-3-pro-image) |
GPT Image 2 (gpt-image-2) |
|
|---|---|---|
| Inputs | Text + up to 14 reference images: up to 6 objects at high fidelity, up to 5 characters, up to 3 style references | Text + one or more reference images. All image inputs are processed at high fidelity automatically |
| Output size | 1K, 2K or 4K. Aspect ratio follows the input image unless set | Flexible: edges in multiples of 16 px, max 3840 px, ratio up to 3:1. Popular sizes include 1024×1024, 1536×1024, 2048×2048 and 3840×2160 |
| Quality setting | Resolution tier (1K/2K/4K) |
low, medium, high, auto
|
| Editing | Conversational, multi-turn edits; localized changes to lighting, focus, angle, color | Edits endpoint with masks (inpainting); multi-turn editing via the Responses API |
| Text in images | Google highlights legible text and translating text inside images for other locales | OpenAI says text rendering is improved but placement and clarity can still miss |
| Extras | Grounding with Google Search; a "thinking" step for complex prompts | Batch API support |
| Provenance | All outputs carry an invisible SynthID watermark | Check OpenAI's current documentation for provenance details |
| Documented limits | Can struggle with small faces, spelling and fine detail; complex blends can look unnatural | Complex prompts up to ~2 min; text placement; consistency across generations; precise layout |
Job by job
1. Clean packshots and catalog consistency
You want the same product, lit the same way, across a set of images.
- GPT Image 2 works well when you already have a decent photo and want controlled changes: tidy the background, fix the lighting, extend the canvas to a marketplace ratio. Because it always processes inputs at high fidelity, product details carry through edits. OpenAI notes this can raise input-token costs on edit requests.
- Nano Banana Pro is useful when you need a series of variants, such as the same bottle in several colorways or angles. Its multi-turn editing and object-fidelity references help keep the product stable across the set.
Marketplaces often have strict rules for main images (background, cropping, added text). Check them before you generate, and keep a real photo as the source of truth.
2. Lifestyle scenes
You want the product on a kitchen counter, a beach towel or a desk setup.
- Nano Banana Pro has the edge on paper for composites. You can supply the product, a model, a prop and a style reference in one request, within the documented limits of 6 high-fidelity objects, 5 characters and 3 style images.
- GPT Image 2 handles reference-based scenes too. OpenAI's own example combines four product images into one gift-basket shot. Inpainting a new background around a masked product is a dependable way to leave the product pixels largely alone.
3. Labels, packaging text and localized ads
This is where the two models differ most in emphasis. Google markets Nano Banana Pro around clear text and localization. Its examples include translating can labels into another language while keeping everything else the same, and adapting a poster to a new market. OpenAI's guide is more cautious and lists text placement and clarity as a known limitation.
In practice, don't trust either model with regulated text. Ingredient lists, dosages, certifications and legal copy should be added as real text in a design tool. Use the model for headlines and mood, and proofread everything.
4. Precise edits to an existing photo
You want to remove a stray cable, swap the backdrop, or change the strap color only.
- GPT Image 2's mask-based inpainting is built for this. You mark the area, describe the change, and leave the rest.
- Nano Banana Pro supports localized edits through conversation. Google notes that masked editing and major lighting changes can sometimes produce artifacts.
5. Ad creatives at unusual sizes
Banners, marketplace headers and story frames often need awkward dimensions. GPT Image 2's custom WIDTHxHEIGHT sizing (up to 3:1, max 3840 px edge) covers many of them directly. Nano Banana Pro offers 1K/2K/4K with set aspect ratios, and Google demonstrates outpainting to new ratios while keeping the subject in place.
6. First frames for video
If the image will be animated later with a model like Veo 3.1 or Kling 3.0, generate it at the video's aspect ratio and at least at the video's resolution. Nano Banana Pro's 4K tier and GPT Image 2's 4K sizes both cover this. Google's own Veo docs show Nano Banana images used as Veo reference images.
Summary: which to pick
| Job | Lean toward |
|---|---|
| Multi-reference composite (product + model + props + style) | Nano Banana Pro |
| Localized versions of the same creative | Nano Banana Pro |
| Masked edit on an existing photo | GPT Image 2 |
| Unusual banner sizes | GPT Image 2 |
| Colorway or angle variant sets | Nano Banana Pro (test GPT Image 2 too) |
| Background cleanup of a real packshot | GPT Image 2 (test Nano Banana Pro too) |
| First frame for image-to-video | Either, at the video's ratio |
These are tendencies based on the documented features, not benchmark results. The best way to decide is to send your own product photo and brief to both.
Keep it honest
AI product images still have to show the product the customer will receive. Don't add features, change proportions, or improve materials beyond reality. Check the platform's rules on AI-generated or edited imagery. Keep the original photos on file.
Testing both without two accounts
Sora2 Hub is a credit-based multi-model studio where Nano Banana Pro and GPT Image 2 sit alongside video models such as Veo 3.1, Kling 3.0 and Seedance 2.0. You can run the same product brief through both image models, keep the better result, and animate it, all from one credit balance.
Sources: Google DeepMind Nano Banana Pro page; Google Gemini API "Nano Banana image generation" docs (features, reference-image limits, limitations); OpenAI GPT-Image-2 model page and image generation guide (sizes, quality, input fidelity, edits, limitations); Google Gemini API Veo 3.1 docs. Checked October 2026.
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