Direct answer: Based on our analysis, Google Gemini is the best solution for product images in the home and furnishings sector. Products are automatically optimized, colors are accurately reproduced, and usable results are achieved on the first attempt. OpenAI stays too close to the original image, shows color inconsistencies between outputs, and in some cases, even adds non-existent text to products.
Home and decor brands face a recurring problem: every new collection requires professional product images. But traditional product photography with setup, lighting, and post-processing takes time and money.
AI-generated images promise an alternative. But are they truly suitable for online shops selling ceramics, vases, jugs, and decorative objects that demand high color fidelity and dimensional accuracy? We thoroughly tested Google Gemini (Nano Banana 2) and OpenAI GPT Image 1.5. The result: AI-generated product images generally work for home and decor shops. However, the models differ in crucial aspects that determine their practical suitability.
- What we tested: products, image types, and AI models
- Legal basis: Are you allowed to use AI-generated images commercially?
- Google Gemini for Home & Decor: Appealing presentation and consistent output
- OpenAI for Home & Decor: Product loyalty, but risky peculiarities
- Our recommendation: When to use which model for home & decoration.
1. What we tested: products, image types, and AI models
Two image types in the test
- Studio shots: Product images on a neutral background, isolated and clean.
- Model shots: A model holds the product in a staged situation.
Two AI models in direct comparison
We tested Google Gemini (Nano Banana 2) and OpenAI GPT Image 1.5 in parallel. Both models received identical prompts.
A key testing principle: We deliberately did not use perfect source photos. Unfavorable perspectives, skewed shots, and suboptimal lighting conditions show how the models handle realistic input and where the differences lie.
2. Legal basis: Are you allowed to use AI-generated images commercially?
AI-generated images from Google Gemini and OpenAI can generally be used commercially. According to their current terms of service, neither provider claims ownership rights to the generated output.
Ownership rights and commercial use
| Google Gemini | OpenAI GPT Image | |
|---|---|---|
| Property rights | For the user | For the user |
| Commercial use | Allowed (within the framework of the API and business terms) | Allowed (within the framework of the API and business terms) |
| Legal protection in copyright lawsuits | Yes (IP indemnity when using the Business/API and with an active safety filter) | Yes (Copyright Shield when using the Business/API) |
| Training with output | No (when using Business/API) | No (when using Business/API) |
Copyright classification
Under current German law, AI-generated images are often not protected as independent copyrighted works if they lack sufficient originality. Exclusivity against third parties is therefore not guaranteed. Similar outputs can also be created by other users.
What Home & Decor Brands Should Pay Special Attention To
Product images must convey an authentic impression of the product. In practice, discrepancies can lead to returns if customers perceive the actual product differently than in the online shop image. Retailers who want to use AI-generated product images should keep this in mind.
Additionally, AI-generated images should always be checked to ensure the model isn't hallucinating text or labels, i.e., inventing non-existent content. In our tests, OpenAI added a non-existent inscription to a vase. Such outputs must not be used in the shop without prior verification.
Labelling requirement from 2 August 2026 (EU AI Act)
- Providers must make AI-generated content machine-readable and identifiable.
- A labeling concept for AI-generated product images should be planned early on.
- The specific implementation in e-commerce is currently being further defined.
3. Google Gemini for Home & Decor: Appealing presentation and consistent output
In our tests, Google Gemini delivered usable product images for home and decor. Products were automatically optimized, colors were accurately reproduced, and results were achieved on the first attempt.
Patterned mug (Studio: 9/10, Model: 8/10)
Observations
Gemini straightens the cup and improves the perspective compared to the original image. The specified pink background color code from the prompt is precisely matched and remains consistent across all images. The flower details are rendered with accurate color. In the model shot, Gemini presents the product more freely: The hand holds the cup in a different, more appealing position than in the original photo.
Jug (Studio: 8/10, Model: 8/10)
Observations
The jug is depicted as rounder and more refined than in the original. In the model shot, Gemini presents the product again more freely with an independent perspective instead of simply copying the original image. Both outputs are usable, and format specifications are adhered to.
Vase (Studio: 5/10, Model: 5/10)
Observations
The unusual vase shape in the studio output is likely due to the suboptimal source image. A strong shadow in the model image is noticeable and can be perceived as distracting depending on the setting. Using multiple input images (e.g., from below or the side) would significantly improve the studio output.
Which works perfectly
- Automatic optimization: Products that are skewed or photographed poorly are straightened and perspectively improved in the output.
- Color precision: Exact hex codes are precisely matched and maintained consistently across all outputs (crucial for consistent settings and brand-relevant product colors)
- Free staging: Gemini does not adopt the original perspective 1:1, but develops independent, more appealing image compositions.
- Single-shot capability: Usable results are achieved in the first attempt, without multiple iterations.
- Format fidelity: Strictly adheres to prompt specifications (e.g., square format for Shopify)
borders
- Input quality is critical: If there is only one source photo with an unfavorable perspective (as with the vase), the shape in the output may appear unusual.
- Strong shadows in model shots can be distracting depending on the setting.
Recommended Use Cases
Home and decor brands that require consistent color fidelity across multiple products, value clean presentation, and work with real retailer photos (not optimized studio shots). Especially suitable for ceramics, vases, and patterned products without text or label requirements.
4. OpenAI for Home & Decor: Product loyalty, but risky peculiarities
OpenAI delivers solid studio shots for simple product shapes and stays very close to the original image. However, the lack of automatic optimization, color inconsistencies, and the risk of text distortion result in increased testing effort in production environments.
Patterned mug (Studio: 4/10, Model: 5/10)
Observations
OpenAI adopts the angle and perspective of the original photo almost perfectly. The pink hue from the prompt deviates slightly – and critically, the flower details are rendered yellowish instead of orange. In an e-commerce context, this is a significant problem, as customers may perceive the actual product differently. In the model shot, OpenAI maintains a strong focus on the product but adopts the perspective exactly from the original image.
Jug (Studio: 3/10, Model: 6/10)
Observations
The studio output is solid and close to the original. However, in the model shot, OpenAI ignores the format specification and delivers a portrait orientation instead of a square one, despite explicit instructions in the prompt. While the staging in the model image itself appears coherent, it uses the exact same perspective as the studio shot.
Vase and text hallucination (Studio: 2/10, Model: 7/10)
Observations
The studio output reveals a structural risk: OpenAI adds non-existent lettering to the vase. When asked, the model cannot explain why. For production use, this is a deal-breaker without manual review. In contrast, in the model shot, OpenAI delivers a more realistic product representation in terms of color and proportions than Gemini.
What works well
- Product shapes are accurately represented in simple 3D objects without text.
- Suitable for individual images with high template requirements (e.g., exact angle as in the original).
- Suitable for studio shots of uncomplicated products.
Critical weaknesses
- Copies input errors 1:1: Unfavorable perspectives and skewed shots remain uncorrected.
- Color inconsistency: Slight color deviations between outputs and compared to the original, risky for brand-critical product colors.
- Partially ignores format specifications: portrait orientation instead of square despite explicit prompt instruction.
- Text hallucination: In isolated cases, it invents non-existent labels on products.
- Each output must be manually checked (no single-shot capability like with Gemini)
- No independent staging: Model shots adopt the pose and perspective of the studio shot, instead of re-staging the product.
Recommended Use Cases
Individual studio shots for products where the exact original perspective needs to be maintained. Due to text distortion and color inconsistency, not suitable for untested use in series production or with brand-critical product colors.
5. Our recommendation: When to use which model for home & decoration?
Based on our tests, we recommend Google Gemini (Nano Banana 2) as the primary solution for home and decor brands. OpenAI is suitable for specific individual cases, but carries structural risks that generate additional testing effort in production environments.
Google Gemini as the main solution
In tests, Google Gemini delivers consistent product renderings across multiple items. Its automatic optimization corrects suboptimal source images. This is a crucial advantage when retailers are working with actual store or mobile phone photos as source material.
We especially recommend Google Gemini in these cases:
- Product lines with multiple decorative objects in defined brand colors
- Studio shots where the source material is not perfect
- Model shots that require a free, appealing re-enactment
- Brands with a limited budget for professional product photography
Not suitable: Products for which only a single source photo from one perspective is available. In these cases, the risk of unusual product shapes in the output increases significantly.
Gemini – Qualitative Assessment
| category | Score (1–5) | Reason |
|---|---|---|
| To use | 5 | High benefits for home and decor brands due to color accuracy, automatic optimization, and flexible presentation options. Particularly relevant for product lines with multiple items in brand colors. |
| Time saving / efficiency | 5 | A high success rate on the first attempt significantly reduces iteration effort. |
| Implementation effort (5 = very easy) | 4 | Structured prompts and multiple input photos per product are necessary. Afterwards, it is easily reproducible. |
| Scalability | 5 | Highly scalable thanks to reusable prompt and setting structure. No significantly increasing additional effort per additional product. |
OpenAI for specific individual cases
OpenAI is only suitable for home and decor applications if the exact original perspective needs to be maintained. The increased verification effort required due to text distortion and color inconsistencies makes its use in series production inefficient.
OpenAI – Qualitative Assessment
| category | Score (1–5) | Reason |
|---|---|---|
| To use | 3 | Limited usability due to color inconsistency, text distortion, and lack of optimization performance. Suitable only for non-critical individual products. |
| Time saving / efficiency | 3 | Multiple iterations and manual verification significantly increase the effort compared to Gemini. |
| Implementation effort (5 = very easy) | 3 | Similar to Gemini, but more complex to operate due to inconsistency and mandatory testing. |
| Scalability | 2 | Due to the risk of hallucinations and inconsistent output for series, it is not stably scalable. |
