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published dec 17, 2025
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This guide shows you how to compare Google’s Nano Banana Pro with ChatGPT’s image generation model across a consistent set of use cases. You’ll build a reusable comparison matrix, run the same prompts through each model, and score the results across consistency, creativity, utility, and quality. The example test uses five use cases and generates 4 images per model.
The source comparison also includes Google’s advanced image generation tool in AI Studio as an optional test.
You’ll create a reusable model-comparison workflow and scoring matrix that can be used for future image model tests.
If you don’t have Gemini Pro, you can use Nano Banana Pro for free. Try opening an incognito browser if you have trouble.
Decide which image-generation use cases you want to compare. The example comparison uses five:
Outline your testing rules before you generate anything. The example rules are:
Feed your use cases into Claude, ChatGPT, or Gemini and ask it to generate structured JSON prompts for each one.
Here’s my use cases: [use cases]
Write me a JSON prompt for each of the 5 use cases. Invent a different, fun brand for each use case. For the arbitrary image, do something detailed and easy to see if it's fake, like a crowd of sports fans at an NBA game. Include instructions to generate 4 variations per prompt in a 4x4 grid.If you want to test this on your own brand, ask the LLM to interview you about your brand and then write prompts for each use case.
To save time and tokens, ask the LLM to write a prompt that generates 4 variations in a 4x4 grid.
Create a scoring matrix for your comparison.
Use this structure: | Use Case | Model | Consistency | Creativity | Utility | Quality | Overall Rating | |---|---|---|---|---|---|---| | Use case 1 | Nano Banana (Gemini) | | | | | | | Use case 2 | ChatGPT Image | | | | | | | Use case 3 | Nano Banana Pro (Vertex) | | | | | |
Feed each use-case prompt into each tool and generate the images.
Open a new chat for each use case so you clear the context between tests.
Rate each output against the criteria in your scoring matrix.
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