Midjourney vs Nano Banana Pro vs FLUX 2 Max: Comparing AI Image Generators in 2026
The BotHub blog team tested six AI image generators: Midjourney v7, Qwen Image 2 Pro, FLUX 2 Max, Nano Banana Pro, GPT Image 2 and Grok Imagine. They ran identical prompts through six scenarios and scored the output on four criteria. The authors don't say which model won, but they admit the outcome surprised even them.
AI-processed from Habr AI; edited by Hamidun News
The BotHub blog team on Habr compared six AI image generators: Midjourney v7, Qwen Image 2 Pro, FLUX 2 Max, Nano Banana Pro, GPT Image 2 and Grok Imagine. The team ran identical prompts through six use cases and scored the results on four criteria. The authors say the winner came as a surprise to them.
Which models made the cut
BotHub's test included six image generation models that are currently most often named among the market leaders.
- Midjourney v7
- Qwen Image 2 Pro
- FLUX 2 Max
- Nano Banana Pro
- GPT Image 2
- Grok Imagine, tested in image quality mode
All six models received the same prompt in each of the six scenarios. This setup removes differences in prompt wording, which usually get in the way of a fair head-to-head comparison of image generators. The lineup also spans several approaches to image generation that compete for the same audience in 2026, from photorealistic scenes to illustrative styles.
How the testing worked
The test used six use cases and four scoring criteria, applied identically to every model.
- 6 image generation models compared
- 6 use cases tested on each model
- The same prompt for every model within a given scenario
- Results scored on 4 criteria
- The authors compiled the final set of images for comparison into a single article on Habr
"We had 6 generation models, 6 use cases, the same prompt for every model, and 4 criteria for scoring the results," the
BotHub blog post on Habr says.
The authors state plainly that the test is meant to let readers compare the images with their own eyes instead of relying on the promotional screenshots that each model's developer publishes.
Who won
The publicly available part of the article doesn't reveal which model scored highest across the four criteria. The authors only note that the result surprised them.
"The winner turned out to be unexpected," the
BotHub blog authors write.
The authors moved the detailed breakdown, with images for each of the six scenarios and an explanation of how the four criteria were scored, into the main article on Habr. The short announcement covers only the test format and the participants, not the final results table.
What it means
The AI image generation market has grown so crowded that choosing a tool now requires direct comparison on identical tasks and identical prompts, not trust in the promotional samples each developer shows on its own blog. BotHub's format of six models, six scenarios, the same prompt and four scoring criteria also gives a template for testing future image generator releases.
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Key Questions Answered
Which models made the cut?
BotHub's test included six image generation models that are currently most often named among the market leaders. Midjourney v7 Qwen Image 2 Pro FLUX 2 Max Nano Banana Pro GPT Image 2 Grok Imagine, tested in image quality mode All six models received the same prompt in each of the six scenarios. This setup removes differences in prompt wording, which usually get in the way of a fair head-to-head comparison of image generators. The lineup also spans several approaches to image generation that compete for the same audience in 2026, from photorealistic scenes to illustrative st
How the testing worked?
The test used six use cases and four scoring criteria, applied identically to every model. 6 image generation models compared 6 use cases tested on each model The same prompt for every model within a given scenario Results scored on 4 criteria The authors compiled the final set of images for comparison into a single article on Habr "We had 6 generation models, 6 use cases, the same prompt for every model, and 4 criteria for scoring the results," the BotHub blog post on Habr says. The authors state plainly that the test is meant to let readers compare the images with their o
Who won?
The publicly available part of the article doesn't reveal which model scored highest across the four criteria. The authors only note that the result surprised them. "The winner turned out to be unexpected," the BotHub blog authors write. The authors moved the detailed breakdown, with images for each of the six scenarios and an explanation of how the four criteria were scored, into the main article on Habr. The short announcement covers only the test format and the participants, not the final results table.
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