ChatGPT vs Gemini Nano Banana: Which AI Is Better for Architecture & Landscape Design?

The two biggest AI image generation models right now are ChatGPT 5.5 and Gemini with Nano Banana Pro. But which one is actually better? If you could only choose one and actually pay for it, which one would you pick? To find out in this video, I'm putting both models head-to-head across real-world workflows in architecture, landscape, and urban design. Let's get started! To begin, let me explain the testing method I'm using. I'll put ChatGPT 5.5 on the left side of the screen and Google Gemini with Nano Banana Pro on the right. Throughout the video, I'll be using prompts from my AI prompts guide, which contains a complete collection of prompts covering the entire design process. In the guide, I've organized workflows for architecture, landscape architecture, and urban design, covering everything from concept development and site analysis to rendering and presentation graphics. For each test, I'll use the same input image and the exact same prompt, then compare the results side by side. For example, if I want to create a mapping presentation board, I'll go to the mapping chapter, upload the same base image to both models, paste the same prompt, and compare the outputs. This way, we can evaluate the differences as fairly as possible and see which model performs better for design workflows. First, let's start with mapping and site analysis. For this test, I'm using the same Google Maps screenshot and the exact same prompt for both models. In a prompt, I provide detailed instructions on the layers I want the AI to extract, including the road network, landscape structure, functional zoning, and node analysis. Then I run the prompt in both ChatGPT and Gemini. Here are the results. In terms of content, both models follow the layout and sections I requested. They successfully extract the key information and organize it into a presentation board format. However, I would say ChatGPT performs better in this test. The color palette feels more refined, the graphics are cleaner, and the overall level of detail is stronger. The final board also looks more polished and presentation-ready, especially for design reviews and portfolio work. For another test, I created a more detailed site analysis board, including data visualization, flat maps, axonometric diagrams, and existing site photos. Once again, I used the same inputs and the same prompt for both models. In this comparison, ChatGPT clearly performs better. Next, I switched to a completely different site and asked both models to generate a landscape architecture version of the analysis board. I wanted to see how well they could adapt the same workflow to a different context. And once again, ChatGPT delivered outstanding results. The board feels more tailored to landscape design, with stronger graphics, better organization, and a more polished presentation style. So, for mapping site analysis and presentation boards, ChatGPT takes the lead in my opinion. For the minimal style test, both models follow the color palette I specified in the prompt. However, NanoBanana wasn't very accurate this time. It distorted the base map and changed some of the original geometry. Here are a few different mapping styles generated with ChatGPT, all based on the same source map but presenting different types of content. NanoBanana struggled to stay accurate to the original map. So what if I remove the base map entirely and just provide a location instead? For this test, I use a very simple prompt. Which result do you think is better? Drop your comment below. For site analysis boards with maps and activity collages, here's the prompt I used. I also tested historical collage maps. For this category, I actually prefer NanoBanana. ChatGPT tends to add too much detail, while NanoBanana feels more balanced overall. The composition, graphics, and information density work together better, making the final board easier to read and visually more appealing. Next is probably the most common use case for AI. Architecture renderings. We'll start by comparing sketch to rendering results. Here's a quick tip. If you want more consistent and higher quality results, avoid prompts like render my building. AI doesn't really know what a good rendering means. What it does understand are the details you provide. Things like facade materials, lighting, atmosphere, landscaping, and the surrounding environment. So the more specific you are, the better the result. Let's look at the outputs. NanoBanana slightly altered the form of my building in this first test. It's not a huge change, but it doesn't stay completely faithful to the original sketch. However, in the next example, its performance is much better. Going from a model to a rendering is easy for both of them. You can try different facade materials, lighting conditions, and atmospheres, and both models will generate strong rendering options. From my perspective, the results are generally very good. You can even start with a very simple massive model and describe the architectural details in the prompt. Then the AI can generate fairly accurate architecture and landscape renderings based on your description. You can also use AI to change the facade design, generate different viewpoints, zoom into specific areas, create interior perspectives, produce bird's eye views, and even transform a conceptual model into a realistic real-world visualization. Last, I compared a more traditional architecture rendering workflow, including animation generation. Landscape architecture and urban design plans, along with perspective renderings, are probably the workflows I use most often. I've shared the prompts and input images here, so I'll leave it up to you to compare the results and decide which one you prefer. Personally, I think this comes down to taste. Both models are capable of producing strong results, and a lot of it depends on the overall style and vibe you're looking for. In my previous video, I shared my complete landscape design workflow using Gemini Nano Banana. I started with a simple landscape plan sketch, then used AI to generate all the renderings, 3D models, diagrams, and animations through the right prompts and workflow. If you'd like to see the full process, you can find the video linked here. Then we come to my favorite part, planting diagrams. I like to start by giving the AI a specific role, such as a planting designer or horticultural expert. Then I ask it to guide me through the process by requesting information like the project location, sun exposure, soil conditions, maintenance requirements, and other side factors. Based on those inputs, the AI can generate a planting list that is suitable for the site. After I approve the planting list, the AI follows my prompts to generate the planting diagrams. It uses the styles, perspectives, layouts, and graphic language that I specify. So the first response from the AI is usually a set of questions for me to answer. In this example, I provide fairly simple answers, but in a real project, I would include much more detailed site information and constraints. Based on that, the AI generates a planting list including plant quantities, mature heights, and other planting details. Of course, I always recommend double checking the plant selections. The layering strategy is especially important and should be reviewed carefully. For the text-based part of the workflow, both ChatGPT and GemKnight follow the instructions very well. The planting recommendations look solid from both models. Once everything is confirmed, we can let them process the images and generate the planting diagrams. Both models generated the planting lists and diagrams as requested. The seasonal charts look great, and honestly, I can't really pick a favorite between the two. For planting plans though, I would say ChatGPT is slightly more accurate overall. Using the same workflow, asking the AI to generate a planting list first, we can then create different types of diagrams, including planting plan sections, elevations, and axonometric drawings. We can also use this for garden design. For example, I can ask the AI to suggest plans for a living area, provide a budget, and define my design goals. Using the prompts, the AI can generate planting diagrams, plan schedules, and even place the selected plan directly into an existing site photo. The last category is image editing. Once we have a rendering or if we want to change materials on an existing photo, we can use ChatGPT or NanoBanana instead of Photoshop. For material replacement, whether it's paving, facade materials, or other surface textures, both models perform very well for more complex 3D-related edits, such as applying graphics or images onto perspective surfaces like stairs, walls, or other architectural elements. ChatGPT performs a bit better. Both models are also capable of transforming renderings into different illustration styles, giving you multiple presentation options from the same base image. So that's the comparison for today. What do you think? Which one do you prefer? ChatGPT 5.5 or Gemini NanoBanana Pro? Let me know in the comments. If you'd like to try these workflows yourself, you can find the full collection of prompts I tested in my AI Prompt Guide. It covers every stage of the design process from site analysis and mapping to renderings, planting design, diagrams, presentations, and image editing. You'll find the link in the description below. Thanks for watching and I'll see you in the next video.