AI Image Generation Models Compared: Qwen, GPT-4 Vision, Seadream, Nano Banana

Introduction

The landscape of artificial intelligence image generation has evolved dramatically, with multiple sophisticated models now competing to deliver the most realistic and contextually accurate composite images. As businesses and creators increasingly rely on AI-powered visual content generation, understanding the strengths and limitations of different models becomes essential for making informed decisions about which tool to deploy for specific tasks. This comprehensive analysis examines four leading AI image generation models—Qwen ImageEdit Plus, Nano Banana, GPT Image 1, and Seadream—through rigorous testing across diverse scenarios ranging from simple environmental compositing to complex anatomical precision requirements. By evaluating these models across real-world use cases, we can identify which solutions excel in particular domains and where each model’s capabilities shine brightest.

Understanding AI Image Generation and Composition

Artificial intelligence image generation has transitioned from experimental technology to practical business tool, enabling creators to composite multiple images, adjust lighting, and create realistic scenes that would traditionally require extensive manual work in design software. At its core, AI image generation involves training neural networks on vast datasets of images to learn patterns, lighting physics, spatial relationships, and visual aesthetics. When given a prompt and source images, these models must understand not just what objects look like, but how they interact with their environment—how light reflects off surfaces, how shadows fall, how materials respond to different lighting conditions, and how objects naturally position themselves in space. The sophistication of modern models lies in their ability to maintain consistency across multiple elements: the lighting on a composited object must match the background environment, the shadows must fall in physically plausible directions, and the overall aesthetic must feel cohesive rather than obviously artificial. This requires the model to perform complex reasoning about three-dimensional space, physics, and visual design principles, all while generating pixels in real-time based on probabilistic predictions.

Why Image Composition Quality Matters for Businesses

The quality of AI-generated composite images directly impacts brand perception, marketing effectiveness, and professional credibility. When a business uses AI-generated imagery for marketing materials, product presentations, or design work, any obvious artificiality or unrealistic elements immediately undermines trust and professionalism. High-quality image composition—where elements are seamlessly integrated with proper lighting, shadows, and environmental consistency—appears natural and professional, while poor composition reveals the artificial nature of the work and appears unprofessional. For e-commerce businesses, real estate marketing, product visualization, and advertising, the difference between a well-composed image and a poorly-composed one can significantly impact conversion rates and customer perception. Additionally, as AI-generated content becomes more prevalent, the bar for quality continues to rise; audiences have become increasingly sophisticated at detecting artificial imagery, making technical excellence in lighting, anatomy, and environmental integration more important than ever. Companies that invest in understanding which models produce the highest quality results for their specific use cases gain competitive advantages in content production speed and quality consistency.

The Four Contenders: Model Overview

The four models tested in this comprehensive analysis represent different approaches to AI image generation, each with distinct architectural choices and training methodologies. Qwen ImageEdit Plus, developed by Alibaba’s Qwen team, represents the latest advancement in open-source image generation technology, offering impressive environmental integration and lighting effects. Nano Banana, while capable, generally underperforms in lighting accuracy and environmental consistency compared to its competitors. GPT Image 1, OpenAI’s offering, prioritizes style consistency and lighting accuracy, often producing the most polished and professional-looking results despite sometimes appearing less photorealistic. Seadream excels at atmospheric effects and texture realism, particularly when dealing with complex environmental elements like mist, water, and atmospheric conditions. Understanding these models’ individual strengths and weaknesses allows users to select the appropriate tool for their specific requirements rather than assuming one model works best for all scenarios.

Real-World Testing: Environmental Compositing

Portrait in Waterfall Setting

The first test involved compositing a portrait of a woman into a waterfall scene with the prompt “composite portrait into waterfall setting with matching natural lighting and mist effects.” This scenario tests multiple critical capabilities: the model must understand how to position a human figure naturally within a landscape, match the lighting from the waterfall environment onto the subject’s face and body, and create realistic mist effects that enhance rather than obscure the composition. Qwen ImageEdit Plus produced a competent result with the woman standing in front of the waterfall, though the lighting appeared somewhat flat and unconvincing. Nano Banana failed significantly in this test, placing the woman weirdly within the water itself rather than in front of it, with horrible lighting that made the composition appear obviously artificial. Seadream took a different approach, adding substantial mist that actually helped mask the unrealistic elements by obscuring how the subject’s hair and body morphed into the water—a clever workaround that improved perceived realism through strategic obscuration. GPT Image 1 delivered the superior result, with the woman positioned naturally in front of the waterfall and lighting that appeared genuinely convincing, as if she were actually standing in that location. The lighting on her face changed completely from the source image, appearing to come from the waterfall environment rather than the original portrait lighting, creating a seamless integration that felt authentic.

SUV in Desert Environment

The second environmental test involved placing an SUV into a desert scene with the prompt “transport SUV to desert with accurate sand displacement, heat, haze, and harsh lighting.” This test evaluates the model’s ability to handle extreme environmental conditions, create convincing heat effects, and integrate vehicle lighting with harsh sunlight. Qwen ImageEdit Plus excelled in this scenario, producing phenomenal results with intense sunlight blasting off the SUV’s surface, sand appearing to displace realistically, and an overall sense of the vehicle moving through the harsh desert environment. The orange tint and sun-blasted appearance created authentic desert lighting conditions. Nano Banana produced acceptable results but lacked the intensity and environmental integration of Qwen’s output, appearing more like the vehicle was simply placed in the desert rather than existing naturally within it. Seadream delivered solid results with good sun positioning and background building consistency, though with minor distortion artifacts. GPT Image 1, while producing good coloring and lighting, failed to generate convincing heat haze effects or sand displacement, appearing more stylized than photorealistic. For this particular scenario, Qwen ImageEdit Plus demonstrated superior capability in handling extreme environmental conditions and physical effects.

Executive in Modern Office

The third environmental test placed an executive headshot into a modern office space with the prompt “place executive in modern office with perfect interior lighting match and professional context.” This scenario tests the model’s ability to match interior lighting conditions and create professional-looking business imagery. Qwen ImageEdit Plus produced excellent results with the executive sitting naturally in a chair, hand positioned on the desk, and lighting that appeared accurate to the office environment. Nano Banana failed dramatically, simply transposing the headshot over the office image without any attempt at realistic integration or lighting adjustment. Seadream completely failed this test, placing the face directly over the image without any compositional sophistication. GPT Image 1 similarly failed to produce convincing results. This test revealed significant variation in model performance depending on the specific task—Qwen ImageEdit Plus’s dominance in this scenario contrasted sharply with its performance in other tests, suggesting that different models have been optimized for different types of composition tasks.

Advanced Testing: Pushing Model Limits

Puppies at Sunrise Beach

The fourth test involved compositing golden retriever puppies into a sunrise beach scene with the prompt “move puppies to sunrise beach with golden hour lighting, sand interaction, and coastal atmosphere.” This scenario tests the model’s ability to handle warm, golden lighting conditions and create natural interactions between subjects and environmental elements. Nano Banana produced a complete failure with horrible lighting that appeared amateurish and unconvincing. Qwen ImageEdit Plus delivered good results with realistic puppies and flawless lighting, though the puppies appeared slightly less realistic than in other models’ outputs. Seadream produced what many would consider the best result, with exceptional realism in the puppies, water, and lighting, creating a cohesive beach scene with golden hour atmosphere that felt authentic and professional. GPT Image 1 produced solid second-place results but didn’t match Seadream’s overall quality. This test demonstrated that Seadream excels at atmospheric and lighting conditions, particularly in warm, golden-hour scenarios.

Cat on Furniture with Christmas Context

The fifth test placed a cat on furniture with the prompt “position cat naturally on furniture with realistic physics and domestic lighting,” with an interesting twist—the prompt didn’t explicitly mention the Christmas tree visible in one source image. This test evaluated whether models would incorporate contextual elements and how they handled domestic lighting scenarios. Interestingly, only one of the four models included the Christmas tree in the output, suggesting that models interpret prompts quite literally and don’t always infer contextual elements from source images. Qwen ImageEdit Plus produced a very realistic cat with excellent couch rendering and nice background blur, creating a convincing domestic scene. Nano Banana delivered similarly good results with different lighting and couch styling but equally realistic cat rendering. Seadream produced pretty good results, while GPT Image 1 delivered another strong output. All four models produced passable results in this scenario, with the choice between them coming down to aesthetic preference rather than technical failure. If forced to choose, Qwen ImageEdit Plus’s result appeared slightly superior due to the realistic cat rendering and natural furniture positioning.

Mechanical Watch on Bedside Table

The sixth test involved placing a mechanical watch on a bedside table with the prompt “display watch on bedside table as prized possession with luxury presentation and bedroom lighting.” This scenario tests the model’s ability to handle small objects, maintain proper scale relationships, and create luxury product presentation imagery. Seadream produced a complete failure with the watch appearing the size of the bed, demonstrating a catastrophic failure in scale understanding. Qwen ImageEdit Plus generated a fantastic-looking watch but failed to incorporate the actual bedroom from the source image, instead creating a new environment—technically impressive but not what was requested. Nano Banana produced a watch inside a case on a table that matched the original photo’s table, but didn’t fully achieve the desired composition. GPT Image 1 delivered the best result, most closely aligned with the original images, incorporating the original artwork, blankets, and table while adding a beautiful watch in the foreground. This test highlighted the importance of prompt specificity and the models’ varying abilities to balance realism with compositional accuracy.

FedEx Truck in Urban Environment

The seventh test placed a FedEx truck in an urban environment with the prompt “position delivery truck naturally in urban environment with traffic context and realistic shadows.” This scenario tests the model’s ability to handle large vehicles, maintain environmental consistency, and create realistic shadow physics. Nano Banana produced inconsistent results with good city consistency but oversaturated truck lighting that didn’t match the environment. Qwen ImageEdit Plus delivered really good results with visible buildings, appropriate lighting, and sun positioning that appeared natural. Seadream produced fantastic results with sun coming through behind the truck and matching background buildings. GPT Image 1 delivered excellent results as well, making the choice between Qwen ImageEdit Plus and GPT Image 1 difficult. Ultimately, Qwen ImageEdit Plus’s superior environmental integration and lighting effects gave it a slight edge in this scenario.

Bleeding Edge: Anatomical and Technical Precision

Watch Positioning with Anatomical Precision

The eighth test pushed models to their limits with the prompt “position watch exactly 2.3 centimeters above wrist with anatomically perfect skin deformation and precise shadow physics.” This scenario tests whether models can handle extremely specific technical requirements and anatomical accuracy. Nano Banana failed dramatically with anatomically incorrect hand positioning, missing watch band, and wrong orientation. Qwen ImageEdit Plus produced decent results but the subject’s body was missing entirely—a significant failure. Seadream attempted to output the measurement specification but produced a watch that was way too big with incorrect hand orientation. GPT Image 1 delivered the clear winner, with correct hand orientation, properly positioned watch with band, and anatomically plausible positioning. This test revealed that GPT Image 1 excels at anatomically precise requirements, while other models struggle with highly specific technical specifications.

Coffee Steam Reflection on Laptop Screen

The ninth test involved the prompt “position laptop at exact 23-degree angle showing coffee steam reflection on screen surface” with a cappuccino and someone working on a laptop. This scenario tests the model’s ability to handle precise angles, reflections, and complex physical interactions. All four models struggled with this test, suggesting that precise angle specifications and reflection physics remain challenging for current AI image generation technology. Nano Banana produced half a laptop—an obvious failure. Qwen ImageEdit Plus generated a pretty good result but the reflection wasn’t correct because the laptop wasn’t actually facing the cappuccino. Seadream’s steam looked fake and unconvincing. GPT Image 1 used an old-school MacBook Air but still failed to create convincing reflections. Between the failures, Nano Banana’s result appeared most realistic in terms of overall composition, though it was technically incomplete. This test demonstrated that all models struggle with precise physical specifications and complex reflection physics.

Selective Eye Color Modification

The tenth test involved the prompt “change only left iris to amber while preserving every eyelash, pupil reflection, and corneal micro detail.” This scenario tests the model’s ability to perform precise, localized modifications while preserving fine details. Qwen ImageEdit Plus and Nano Banana both changed both eyes instead of just the left, failing the core requirement. GPT Image 1 correctly changed only the left iris, producing a polished face with smooth appearance. Seadream (referred to as “Cream 4” in the transcript) also correctly changed only the left iris while preserving all texture details, creating a more realistic result. Between the two successful models, Seadream’s result appeared more realistic due to preserved texture, while GPT Image 1’s result appeared more polished but less photorealistic. This test demonstrated that Seadream excels at detail preservation while GPT Image 1 prioritizes polish and smoothness.

Dual Identity Face Composition

The eleventh test involved the prompt “create dual identity face maintaining both complete identities without blending or morphing,” attempting to composite two different faces into a single image. This scenario tests the model’s ability to handle complex compositional requirements without losing individual identity characteristics. The results were mixed, with models struggling to maintain both complete identities without blending or morphing. Qwen ImageEdit Plus produced results more akin to what was desired but with size inconsistencies. Seadream essentially made one face look like the other, losing the woman’s original identity. This test revealed that maintaining multiple distinct identities in a single composition remains a significant challenge for current AI image generation models.

FlowHunt’s Approach to AI Image Generation Workflows

FlowHunt recognizes that different AI image generation models excel in different scenarios, and rather than forcing users to choose a single model, the platform enables seamless integration with multiple models simultaneously. By automating the process of sending prompts and source images to multiple models and comparing results, FlowHunt allows users to select the best output for their specific needs without manual switching between different interfaces. This approach acknowledges the reality revealed by comprehensive testing: there is no universally superior model, but rather models with different strengths that excel in particular domains. FlowHunt’s automation capabilities extend beyond simple model comparison to include workflow optimization, where users can set up rules to automatically route specific types of composition tasks to the models most likely to produce superior results. For businesses generating large volumes of composite imagery, this intelligent routing can significantly improve output quality while reducing manual review and refinement time. Additionally, FlowHunt’s integration with multiple models provides redundancy—if one model fails on a particular task, alternative models are automatically tested, ensuring that users always have viable options rather than being blocked by a single model’s limitations.

Practical Implications and Model Selection Guidelines

Based on comprehensive testing across diverse scenarios, clear patterns emerge regarding which models excel in specific domains. For environmental compositing with emphasis on lighting consistency and style coherence, GPT Image 1 consistently delivers superior results, making it the preferred choice for professional design work where aesthetic polish matters more than photorealistic accuracy. For extreme environmental conditions, heat effects, and sand displacement, Qwen ImageEdit Plus demonstrates superior capability, making it ideal for outdoor product photography and environmental compositing. For atmospheric effects, texture realism, and warm lighting conditions, Seadream excels, making it the best choice for beach scenes, sunset imagery, and scenarios emphasizing atmospheric quality. Nano Banana, while capable of producing acceptable results, generally underperforms compared to its competitors and should be considered a fallback option rather than a primary choice. For anatomically precise requirements and detailed modifications, GPT Image 1 again demonstrates superiority, though all models struggle with extremely specific technical specifications like precise angles and reflection physics.

The practical implication for businesses is that model selection should be task-specific rather than assuming one model works best for all scenarios. A business generating diverse imagery should maintain access to multiple models and route different types of composition tasks to the models most likely to produce superior results. This requires understanding each model’s strengths and limitations, which comprehensive testing like this analysis provides. Additionally, users should recognize that all current models struggle with certain types of requirements—precise angle specifications, complex reflection physics, and maintaining multiple distinct identities in single compositions remain challenging across all tested models. For these edge cases, manual refinement or alternative approaches may be necessary.

Technical Considerations and Limitations

All tested models demonstrate impressive capabilities but also reveal consistent limitations that users should understand before deploying them in production workflows. First, all models struggle with precise technical specifications—when prompts include exact measurements, angles, or specific physical requirements, models tend to interpret these loosely or ignore them entirely. Second, all models have difficulty with complex reflection physics and precise lighting calculations, particularly when reflections must accurately represent specific angles or surface properties. Third, models struggle with maintaining multiple distinct identities or complex compositional requirements that involve multiple subjects with specific spatial relationships. Fourth, lighting consistency remains challenging when source images have dramatically different lighting conditions—models sometimes fail to properly adjust lighting to match environments. Fifth, scale relationships can be problematic, particularly with small objects like watches or jewelry, where models sometimes generate objects that are disproportionately large or small.

Understanding these limitations is crucial for setting realistic expectations and designing prompts that work within each model’s capabilities. Rather than fighting against model limitations, successful users work with them, crafting prompts that emphasize the aspects each model handles well while avoiding scenarios where models consistently fail. For example, rather than requesting precise angle specifications, users might describe the desired composition in more general terms that allow the model flexibility in interpretation. Rather than requesting complex reflections, users might accept simpler lighting conditions that models handle more reliably. This pragmatic approach to prompt engineering significantly improves results across all models.

Conclusion

The comprehensive testing of Qwen ImageEdit Plus, Nano Banana, GPT Image 1, and Seadream reveals that no single model dominates across all image composition scenarios. Instead, each model excels in specific domains: GPT Image 1 for lighting consistency and anatomical precision, Qwen ImageEdit Plus for environmental integration and extreme conditions, Seadream for atmospheric effects and texture realism, and Nano Banana as a capable but generally underperforming alternative. Successful deployment of AI image generation technology requires understanding these distinctions and routing different types of composition tasks to the models most likely to produce superior results. By leveraging multiple models intelligently through platforms like FlowHunt, businesses can maximize output quality while maintaining production efficiency, ensuring that each composition task receives the optimal model for its specific requirements rather than forcing all tasks through a single tool regardless of suitability.