Which AI Model Makes the Best Images?
Why It Matters
For businesses and creators using AI image-editing tools, the findings underscore that model selection should be task-specific and empirically validated; the upcoming open-source testing script will let teams quickly benchmark models against their own use cases.
Summary
The video benchmarks four image-editing AIs—Quen Image Edit (QN Image Edit Plus), Nano Banana, GPT Image 1 and Seadream—across multiple real-photo composite tasks (waterfall portrait, SUV in desert, office headshot, puppies on a beach, cat in a living room, product placement, and a FedEx truck). Results varied by scenario: GPT Image 1 often produced the most stylistically consistent composites and won the portrait and product tests, Quen Image Edit Plus excelled on the SUV/desert and several indoor placements, Seadream delivered the best beach/puppy result, while Nano Banana was inconsistent and sometimes produced obvious artifacts. The presenter notes wide performance variability depending on the prompt and will open-source a script that automates submitting the same prompt and images to all four models for easy comparison. Overall, no single model dominated every task—strengths were context-dependent.
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