eCommerce AI Image Editing: GPT Images & Nano Banana
AI image editing tools analyze and automatically adjust product photos, allowing eCommerce businesses to enhance quality, remove backgrounds, or modify details with minimal effort.
We tested the top 7 AI image editing tools on 20 images and 20 prompts across five dimensions, including prompt adaptability, realism, shadows, color rendering, and image quality.
Benchmark results
See our benchmark methodology and detailed explanation of each tool’s performance.
Examples from our benchmark
Figure 1: Image showing seven different versions of a cushion and blanket scene.
Prompt: “Keep the cactus-pattern pillow in the center. Remove the green pillow on the left side and reconstruct the sofa texture behind it seamlessly. Leave the blanket on the right side untouched.”
This task requires highly selective editing: removing only one object while preserving two others and seamlessly reconstructing the background texture.
Figure 2: Image showing seven different versions of a hand holding a gaming controller.
Prompt: “Keep the gaming controller and the hand exactly as they are. Remove the wooden floor background and replace it with a clean light-grey gradient studio backdrop. Ensure the edges of the hand remain natural, and lighting stays soft and realistic.”
This task requires precise foreground preservation while performing a full background replacement. High scores depended on maintaining hand and controller integrity, clean edge separation, and consistent studio lighting.
Figure 3: Image showing seven different versions of mini figures in front of a rocky terrain.
Prompt: “Remove the second hiker in the blue outfit and leave only the hiker in the hat and red backpack. Rebuild the rocky terrain and background naturally so the scene looks complete.”
This task tests object removal combined with complex background reconstruction. High scores required believable terrain continuity and consistent lighting.
Figure 4: Image showing six different versions of a serum bottle.
Prompt: “Keep the serum bottle intact. Remove the hand holding the bottle and reconstruct the bottle’s missing edges realistically.”
The difficulty here lies in removing the hand while realistically reconstructing the bottle’s missing edges.
Figure 5: Image showing six different versions of a white frame with a green plant scene.
Prompt: “Keep the white picture frame centered. Remove the round glass vase with leaves on the left and the small metal cup on the right. Fill the background and tabletop cleanly with a bright white surface.”
This task emphasizes selective object removal and uniform background reconstruction while preserving the main subject.
Figure 6: Image showing six different versions of a makeup palette and brushes scene.
Prompt: “Keep the makeup palettes and brushes unchanged. Remove all surrounding clutter and background objects. Replace the background with a white surface to create a tidy product showcase. Preserve realistic shadows under the palettes.”
This task requires precise preservation of objects while removing clutter and replacing the background. High scores depended on maintaining palette details, realistic shadows, and avoiding unintended alterations.
Figure 7: Image showing six different versions of a smartwatch on a blurred green background scene.
Prompt: “Keep the smartwatch on the wrist. Change the soft outdoor background to a dark blue studio background.”
This task requires strict foreground preservation while performing a clean background replacement. Tools were evaluated on edge quality, lighting consistency, and avoidance of foreground distortion.
Figure 8: Image showing six different versions of a waterbottle behind slices of lemon.
Prompt: “Keep the large water bottle unchanged. Remove all lemon and orange slices from the wooden board and rebuild the board texture naturally. Keep the teal background untouched.”
This task combines object removal with texture reconstruction while requiring strict preservation of the background.
Figure 9: Image showing six different versions of a wine glass.
Prompt: “Keep the wine glass unchanged. Replace the background with a clean black studio backdrop with a soft spotlight effect. Remove the blurred orange bottle in the background.”
This task requires strict object preservation combined with controlled studio-style background replacement.
AI image editing tools
GPT Image 1.5
GPT Image 1.5 is OpenAI’s updated image generation model available in ChatGPT and via API. It provides faster image generation (up to 4× compared to the previous version), improved instruction following, and more precise image editing that preserves details such as lighting, composition, and subject consistency across edits.
The model also improves dense text rendering, supports a wider range of editing and transformation operations, and offers higher consistency for branded and product imagery. The tool is primarily suitable for design, marketing, and eCommerce image generation use cases.
Benchmark results:
GPT Image 1.5 demonstrated strong performance on the evaluation set, achieving high scores in prompt adaptability, realism, shadow consistency, color rendering, and overall image quality. The model handled most edit requests reliably, particularly when tasks involved preserving primary subjects while removing secondary objects, replacing backgrounds, or making controlled color and material adjustments.
The model was especially effective in structured, product-focused scenes and studio-style edits. It maintained lighting continuity and realistic shadows when reconstructing surfaces or simplifying backgrounds.
Performance declined in scenarios that required complex foreground reconstruction, such as removing objects closely interacting with the main subject or altering reflective wearable items. In these cases, prompt adherence and realism were less consistent, and visual artifacts appeared more frequently.
FLUX.2 Pro (Image Editing)
FLUX.2 Pro is a production-grade image-editing model and supports multi-reference editing with up to nine images. It enables precise compositing, background replacement, and style alignment through natural-language prompts without requiring parameter tuning or masking.
The system provides reliable output quality across sequential edits and offers advanced control through JSON-structured prompts, HEX color specifications, and direct image referencing using the @ syntax. It is intended for automated workflows, eCommerce pipelines, and other high-volume editing environments.
Benchmark results:
FLUX.2 Pro demonstrated consistent performance across almost all evaluation criteria. It achieved strong results in realism, shadow consistency, and color stability and showed reliable adherence to detailed editing instructions.
The model performed effectively in tasks requiring accurate object removal, background reconstruction, and preservation of existing scene elements. Its limitations appeared in a small subset of prompts involving highly specific object isolation or complex scene reconfiguration, where prompt interpretation degraded and output quality decreased.
Nano Banana Pro (Gemini 3 Pro Image)
Nano Banana Pro (also known as Nano Banana 2 and based on Google’s Gemini 3 Pro Image architecture) is an advanced image-generation and editing model. It interprets natural-language instructions without the need for masks or manual selections, supports multi-image composition with up to 14 references, and maintains character consistency across edits.
The model emphasizes semantic understanding of objects, lighting, and composition, enabling precise adjustments such as color changes, scene modifications, and text rendering. It prioritizes quality over speed, outputs up to 4K resolution, and includes SynthID watermarking.
Benchmark results:
Nano Banana Pro delivered high-quality outputs with strong semantic understanding, particularly for tasks involving object-level adjustments, material changes, and preserving lighting conditions. In several cases, its realism and structural accuracy approached or matched those of FLUX.2 Pro.
However, the model underperformed in scenarios requiring comprehensive clutter removal or background simplification. In these cases, prompt adaptability, realism, and shadow accuracy declined, indicating reduced reliability for reconstruction-intensive tasks.
Qwen Image Edit
Qwen Image Edit specializes in accurate text-based modifications, allowing users to transform visual elements through natural-language prompts. It supports commercial use, processes standard image formats, and applies changes such as object replacement or scene alteration with high fidelity.
The model is optimized for semantic understanding of image content and is suitable for prompt-driven editing workflows that require reliable interpretation of complex instructions.
Benchmark results:
Qwen Image Edit produced satisfactory results for prompts involving targeted object modifications or straightforward substitutions, and it maintained acceptable color accuracy and overall image quality in simpler scenes.
Its performance declined in tasks requiring precise geometric reconstruction, detailed background rebuilding, or high-fidelity shadow integration. Across multiple prompts, the model did not fully satisfy the instructions, leading to reduced prompt adaptability and inconsistent realism.
Seedream 4.0 Edit (ByteDance)
Seedream 4.0 is ByteDance’s unified image-generation and image-editing model, designed to handle complex transformations that combine multiple reference images. It can modify clothing, add or remove objects, change backgrounds, and integrate compositional elements into a coherent scene.
The model offers flexible multi-image workflows suitable for advanced creative editing tasks that require consistent visual integration and high-quality output.
Benchmark results:
Seedream 4.0 demonstrated the capability to produce strong outputs in select scenarios, particularly when edits involved relatively simple structural changes or limited-area modifications. It also produced acceptable color rendering and shadow integration in some prompts.
Although it performed well in some areas, the model frequently struggled with complex background reconstruction, precise object removal, and accurate preservation of required scene elements. These limitations resulted in low prompt adaptability and reduced realism in several tasks.
Wan 2.5 Image-to-Image
Wan 2.5 preview is designed to reinterpret existing visuals. It supports commercial use and applies stylistic, atmospheric, or structural transformations while preserving core elements of the source image.
Users can specify detailed scene changes, such as lighting conditions, weather effects, or thematic shifts, and the model produces a revised composition accordingly.
Benchmark results:
Wan 2.5 failed to produce viable results for a substantial portion of the test set (unable to generate 12 of 20 images). The model frequently failed to interpret instructions, reconstruct missing details, or maintain visual coherence, resulting in scores of 0 across multiple prompts. This level of inconsistency significantly limits its suitability for structured or production-oriented image editing workflows.
Key features of AI image editing tools
Object removal and cleanup
Many AI-powered editors help users remove distracting elements from a single image or from multiple images. These features allow you to clean up cables, background clutter, or accidental objects without resorting to complex software. This feature is helpful for content creators working with product photos, personal projects, or any situation where visual continuity matters.
Key points include:
- Remove backgrounds or isolated objects with minimal manual editing.
- Fill in gaps naturally so the final image looks consistent.
- Produce professional-looking results even when starting from basic photos.
Background removal and replacement
A background remover isolates the subject of the photo and allows users to replace the background with solid colors, creative styles, or other images. This works well for product images, portraits, and social content.
Key aspects include:
- Quick background removal without complex tools.
- Ability to replace backgrounds while retaining the original subject’s edge detail.
- Support for multiple formats, allowing you to start editing immediately after upload.
Generative editing
Some advanced AI tools provide generative functions that respond to a text prompt. These functions can extend a scene, add new elements, or reimagine part of the image. Unlike traditional software, this approach reduces the time needed for complex edits.
Applications include:
- Using prompts to generate multiple variations of an idea.
- Extending an image’s borders to fit design needs.
- Adjusting creative styles without high-level design skills.
Automatic enhancement
Automatic enhancement features analyze the image and adjust lighting, color balance, exposure, shadows, and clarity. This helps users enhance photos without relying on complex programs or manual sliders.
These tools can help with:
- Improving image quality in a single step.
- Quick edits on mobile devices or through a simple online tool.
- Enhancing portraits and other image types with minimal input.
Upscaling and noise reduction
If a photo is low-resolution or captured in challenging lighting, an AI image editor can upscale and restore it. These functions improve clarity and reduce noise, making older or low-quality photos more usable.
Capabilities typically include:
- Increasing resolution while protecting fine detail.
- Improving clarity in photos originally captured on mobile devices.
- Preparing images for prints, presentations, or online use.
Batch processing for multiple images
Some photo editing software allows users to edit multiple images at once. This helps maintain visual continuity across product photos, social content, or any project that includes multiple images.
Benefits include:
- Faster workflows for eCommerce or content teams.
- Consistent adjustments are applied across an entire collection.
- Time savings when preparing product photos in multiple formats.
Limitations and what AI editing does not automatically guarantee
AI still requires human judgment
Although an AI image editor can perform advanced corrections, the user still guides the creative process. Artificial intelligence may misinterpret lighting, perspective, or artistic intent, especially in complex edits. A trained eye often improves the outcome. Situations where this matters include:
- Subtle color grading choices.
- Scenes with layered reflections or unusual lighting.
- Projects requiring complete control over small details.
Possibility of an unnatural appearance
Overusing portrait tools or enhancement features may result in heavily modified-looking results. When enhancing portraits, balance is essential to maintain a natural look. Examples include:
- Excessive smoothing that removes texture.
- Strong contrast edits that distort the original mood.
Inconsistent generative results
When relying on a text prompt to transform images or generate multiple variations, the output may contain unintended elements or visual inconsistencies. This can occur in scenes with many objects, complex backgrounds, or intricate patterns.
Quality depends on the original photo
While AI can enhance the quality of an image or upscale it, severely damaged or extremely low-resolution photos may not produce high-quality results. The initial file limits how far enhancement can go. Factors include:
- Motion blur or deep pixelation.
- Photos captured in extremely low light.
Ethics and authenticity considerations
AI tools can replace backgrounds, remove people, or add elements that were not initially present. This raises ethical concerns in fields such as journalism, documentation, and certain personal photos. Users should apply these features responsibly. Considerations include:
- Maintaining authenticity in professional contexts.
- Avoiding misleading edits in sensitive situations.
- Being transparent when images are significantly altered.
Methodology
Tools evaluated
We benchmarked the following models with the endpoints on fal.ai1 :
- flux-2-pro/edit
- nano-banana-pro/edit
- qwen-image-edit/image-to-image
- bytedance/seedream/v4/edit
- wan-25-preview/image-to-image
We also benchmarked:
- gpt-image-1.5
All tools were evaluated in December 2025. The images are gathered from Pexels.2
Dataset and editing objectives
The benchmark utilized a dataset of 20 images representing eCommerce products and lifestyle scenarios. Each image was assigned a unique prompt containing context-dependent editing instructions. These instructions required precise object removal, background reconstruction, and preservation of photorealistic attributes.
Examples of prompt categories include the following:
- Mini figures: Remove the second hiker in the blue outfit and leave only the hiker in the hat and red backpack. Rebuild the rocky terrain and background naturally so the scene looks complete.
- Candles: Keep the two front candles exactly as they are. Remove the green candle in the back completely and fill in the wooden table naturally. Adjust lighting and shadows to stay consistent.
- Room fragrance: Keep the glass fragrance bottle with diffuser sticks exactly as is. Replace the background with a blue and grey gradient and remove the decorative object on the right side. Maintain realistic shadows beneath the bottle.
We aim to ensure a controlled and repeatable testing environment with fine-grained editing capabilities across all tools.
Evaluation criteria
Each generated image was assessed using five criteria. Every criterion was scored on a scale of 1 to 5, with higher values indicating better performance.
1. Prompt adaptability
This criterion measured how accurately each tool followed the specific instructions contained in the prompt. The assessment focused on the correct removal of objects, the preservation of required elements, and the proper execution of environmental modifications.
2. Realism
This criterion evaluated the naturalness of the edited regions relative to the original image. The assessment considered texture continuity, artifact avoidance, and the visual coherence of reconstructed areas.
3. Shadows
This criterion examined the accuracy and consistency of shadows following the applied edits. Elements reviewed included the direction, softness, and integration of shadows within the scene lighting.
4. Color rendering
This criterion assessed whether the resulting image demonstrated accurate and stable color reproduction. The evaluation included vibrancy, consistency with the prompt, and the absence of unnatural shifts.
5. Image quality
This criterion measured the overall technical quality of the output. Areas of focus included resolution, clarity, sharpness preservation, and avoidance of unintended resizing or distortion.
Scoring approach
The total score for each image was calculated by summing the five criteria, resulting in a maximum possible score of 25 points. All tools received identical prompts, enabling consistent comparison across varied editing objectives.
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