Midjourney vs Nano Banana Pro vs FLUX 2 Max: Comparing Image Generation AI Models in 2026

Midjourney vs Nano Banana Pro vs FLUX 2 Max: comparison of neural networks for images in 2026

The BotHub blog team on Habr compared six neural networks for image generation — Midjourney v7, Qwen Image 2 Pro, FLUX 2 Max, Nano Banana Pro, GPT Image 2 and Grok Imagine — running identical prompts through six use case scenarios and evaluating the results by four criteria; according to the authors, the test winner turned out to be unexpected for them.

Which models were included in the comparison

The BotHub test included six image generation models that are currently most often named among 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 — so the test excludes the difference in query formulations, which usually interferes with an honest comparison of image generators among themselves. This set of participants covers several approaches to image generation that compete for the attention of the same audience in 2026 — from photorealistic scenes to illustrative styles.

How testing was conducted

Testing was built on six use case scenarios and four result evaluation criteria, identical for all test participants.

• 6 image generation models in comparison
• 6 use case scenarios tested on each model
• Same prompt for all models within one scenario
• Results were evaluated by 4 criteria
• The authors compiled the final selection of images for comparison into one material on Habr

"We had: 6 models for generation, 6 use case scenarios, for all models — identical prompt and 4 criteria for evaluating the result," the

BotHub blog material says on Habr.

The authors directly point out that the goal of the test is to give the reader the opportunity to compare images with their own eyes, rather than relying on promotional screenshots published by the developers of each individual model.

Who won the comparison

The authors do not reveal who exactly scored more points according to the four criteria — they only note that the result surprised them.

"The winner turned out to be unexpected," note the

BotHub blog authors.

A detailed analysis with images for each of the six scenarios and an explanation of how points were counted by four criteria, the authors placed in the main material on Habr — a short announcement only reveals the format of the test and participants, but not the final results table.

What this means

The market of neural networks for image generation has grown so much that choosing a tool requires direct comparison on identical tasks and identical prompts, rather than trusting the promotional examples that each model developer shows on their own blog. The BotHub test format — six models, six scenarios, the same prompt and four evaluation criteria — itself sets a template for checking future releases of image generators.