GPT-Image-2 vs Nano Banana: An Architect Real-World Head-to-Head Test
Introduction
As soon as GPT-Image-2 launched, I ran a systematic head-to-head comparison with the Nano Banana series — using architectural design as the test arena: renderings, presentation boards, analysis diagrams, storyboards, style transfer — tested one by one.
Bottom line: GPT-Image-2 is NOT a replacement for Nano Banana Pro/2 — it's a complementary tool worth adding to your toolbox. When Banana is unstable, GPT steps in. For simple tasks, it's more than adequate.
- Use GPT first for: Presentation boards, posters, report materials, Chinese text annotations, creative concepts, brand content production.
- Use Banana first for: Aerial views, large-scale planning, storyboard grids, style transfer, complex spatial consistency tasks.
8 Key Comparison Insights
01 — Presentation Boards & Posters: GPT is Currently the Best
This is GPT-Image-2's standout scenario, bar none. Layout logic is clear, section partitioning is reasonable, and the text-image relationship shows genuine design sensibility — not the chaotic AI pile-up you'd expect. Same prompt, Banana outputs "usable", GPT outputs "beautiful."
Case 1: Plant Species Identification Board
Prompt: Create a design board identifying 8 major plant species in this photo, providing clear labels and descriptions for each, including leaf shape, color, height, and typical habitat.
Input image
GPT-Image-2 output
GPT-Image-2 output
Nano Pro output
Case 2: Xiangmi Park Urban Design Board
Prompt: Using the uploaded satellite image of Shenzhen Xiangmi Park, transform the highlighted area into a futuristic Chinese-themed amusement park with roller coasters, rides, and themed architectural elements. Generate a comprehensive urban design board including master plan, cross-section details, eye-level renderings (including night views), detailed planting schemes, massing studies, and urban planning analysis diagrams for traffic circulation, functional zoning, and public space usage. All annotations bilingual Chinese-English.
Input image
GPT-Image-2 output
GPT-Image-2 output
Nano Pro output
02 — Chinese Text Generation: GPT is Currently the Most Accurate
This directly matters for architects in China. Banana has persistent garbled text issues with Chinese characters. GPT-Image-2 shows significant improvement — annotations are accurate, Chinese characters are largely error-free, even in densely packed text on complex boards. For text-heavy visual tasks, GPT is the more reliable choice.
Case 3: Interior Space Plan with Details
Prompt: Based on this interior space, generate a floor plan design description with storyboarded detail views showing specific soft furnishing and cabinetry details.
Input image
GPT-Image-2 output
Nano Pro output
03 — Stronger Semantic Understanding: More Complex Prompts = Better Results
GPT-Image-2 has high fidelity to prompt execution. The more precise and complex your instructions, the more it follows your intent. For experienced architects who can write long prompts, GPT has a higher ceiling.
Case 4: Building Section with Interior Details
Prompt: Based on this building, generate a realistic-style cross-section with interior decoration and perspective, maintaining structural rationality while showing internal functional zones and circulation routes. Remove unnecessary textures and surroundings.
Input image
GPT-Image-2 output
Nano Pro output
Evaluation: Banana's output was clean with accurate floor-level function labels. GPT went beyond the brief — automatically adding small analysis diagrams and function icons. When relevant descriptions were removed from the prompt, these extras disappeared too, confirming GPT's proactive semantic understanding.
GPT's interpretive expansion is an advantage, but requires precise prompt control to avoid generating unwanted content.
04 — Complex Architectural Spaces: Banana is Still the Go-To
This is GPT's most obvious weakness. Aerial views, large-scale spatial understanding, storyboard grids — whenever the task involves complex architectural spatial logic, GPT falls apart. For complex spatial tasks, Banana remains the primary choice.
Case 5: FPV Drone 9-Grid Storyboard
Prompt: Generate a 9-grid storyboard of an FPV drone racing around this building at high speed — descending through clouds, diving to the base, 360° orbit, flying through the building, exiting from the top, and ascending to altitude.
Input image
GPT-Image-2 output
Nano Pro output
Evaluation: Banana's 9 outputs showed strong spatial consistency with logical camera progression — basically usable as video material. GPT's 9 outputs had inconsistent proportions, broken details, and chaotic spatial relationships across all quality tiers.
Storyboard tasks are currently GPT's clear weakness — not recommended for this scenario.
Case 6: Multi-Angle 9-Grid Storyboard
Prompt: Generate 9 different angle storyboards including: ground-level looking up, street perspective, distant wide shot, telephoto detail, lobby interior, balcony, interior-through-glass city view, roof garden, and aerial overview.
Input image
GPT-Image-2 output
Nano Pro output
05 — Bold Color Style: Not Suitable for All Projects
GPT's output tends toward oversaturated, darker tones — a "trying too hard" visual tendency with dramatic lighting and strong color contrast. This is an advantage for creative/atmospheric tasks but problematic for color-accurate modifications requiring faithful original tones. Architects report: fine for early concept exploration, but a headache at the color-precise stage.
Case 7: Style Transfer Rendering
Prompt: Use the rendering style and lighting atmosphere of Image 2 to render Image 1 in high-quality photorealism. Preserve all geometry and spatial layout from Image 1. Transfer color, material, lighting, lens quality and premium urban atmosphere from Image 2.
Input Image 1
Input Image 2
GPT-Image-2 output
Nano Pro output
06 — Unstable Output Ratios, Occasional Low Quality
GPT-Image-2 has a unique quality tiering mechanism — low, medium, high quality levels. Higher quality means cleaner output with fewer AI artifacts. But this parameter is currently only controllable via API; web users get randomly assigned quality tiers, sometimes producing jittery lines and smeared details. Output aspect ratios also occasionally deviate from originals. Using the API with specified quality tiers gives a much more stable experience.
07 — Faster Speed, but No Price Advantage
GPT-Image-2 generates faster than Banana in testing — a plus. But pricing offers no surprises — roughly on par with Nano Banana Pro at ¥1-2 per image for high-quality, large-format output. Compared to Nano Banana 2's ¥0.30 per 1K image after price cuts, GPT isn't competitive on cost. For batch-rendering teams, GPT currently isn't the cost-effective option.
08 — When Banana is Unstable, GPT Can Save the Day
A recent discovery: Banana has been experiencing instability — same prompt works during the day but fails at night, or repeated attempts can't produce satisfactory results. In these cases, GPT-Image-2 can serve as a temporary stand-in, especially for simple single-image tasks and presentation board needs. Running both tools simultaneously as mutual backups is currently the most stable workflow configuration.
Additional Test Cases
Case 8: Execute Text Instructions from Image
Prompt: Generate the work described by the text instructions in this image, and remove the text from the image.
Input image
GPT-Image-2 output
Nano Pro output
Evaluation: Banana executed all instructions precisely with correct proportions. GPT completed most modifications but had a typical issue: the original image contained a person, and GPT couldn't place the figure at the correct scale — either deleting or severely distorting proportions.
For multi-instruction, spatially complex editing tasks, Banana is more stable.
Case 9: Dark-Background Technical Section Board
Prompt: Based on this building, generate a photorealistic dark-background vertical section analysis board including functional module analysis, structural analysis, lighting design analysis, construction layer analysis, with the original image as the main visual occupying ~1/4. All text in English.
Input image
GPT-Image-2 output
Nano Pro output
Case 10: Construction Process Analysis
Prompt: Generate a construction analysis for this building, detailing each step from foundation to main structure, facade construction, and finally soft furnishing and landscaping.
Input image
GPT-Image-2 output
Nano Pro output
Evaluation: Banana produced 5-6 clear evolution steps. GPT delivered nearly 20 steps — from empty lot, excavation, construction to completion, with illustrated details at every stage. Information volume far exceeded expectations.
GPT's expansion capability is both a strength and a risk — for formal presentations, explicitly limit step counts in the prompt, otherwise the output becomes overly dense.
Case 11: Logo-to-Headquarters Design
Prompt: Generate an official architectural headquarters design matching the气质 and style of this logo. The logo name is AIRI Lab.
Input image
GPT-Image-2 output (building headquarters)
GPT-Image-2 output (official website UI design for the headquarters)
GPT-Image-2 output (visitor guide image for the headquarters)
Evaluation: The most surprising test of the entire session. GPT not only accurately understood the logo's design language and generated architectural renderings with water features and industrial-style interiors, but also produced a complete, well-typeset website UI design with legible text.
This kind of purely creative task is completely beyond Banana's capabilities. GPT has a unique advantage here, worth exploring for brand content creation and concept development.
Bonus: AI4ELAB Test
One image from AI4ELAB testing. More in the next article.