New Seedream 4.0 DESTROYS Nano Banana?🍌AI Image Editor Comparison
As AI image editors evolve rapidly, comparing character consistency across models helps creators choose the best tool for complex visual storytelling workflows.
What this lesson covers
The creator compares several leading AI image editors including Nano Banana, ChatGPT, Seedream 4.0, and Flux Context Max. The tests evaluate character consistency, object replacement, and image editing capabilities side-by-side.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:06 ↗
Nano Banana excels primarily due to its near-perfect character consistency and deep understanding of lighting and angles during image editing.
- 01:54 ↗
ChatGPT image generation frequently struggles with maintaining character consistency and adhering to complex multi-subject prompts.
- 02:30 ↗
Seedream 4.0 demonstrates strong prompt adherence, though its character consistency can vary when dealing with multiple subjects in a single frame.
- 05:16 ↗
Using chat-to-edit features on platforms like Open Art allows creators to test multiple image models with identical editing prompts.
Workflow outlined in the video
- Access an aggregation platform like Open Art to test various image generation and editing models side-by-side.
- Upload a reference image of yourself alongside a secondary subject or character to test multi-subject consistency.
- Run identical replacement prompts across models like Nano Banana, Seedream 4.0, and Flux Context Max to evaluate how well they handle props and changes.
- Utilize chat-to-edit functions to test directional adjustments, such as viewing an existing generated character from the right side or behind.
Before you use this workflow
These notes describe the source video at its publication date. Model access, pricing, connectors and interfaces may have changed. Check the original source and the provider’s current documentation before installing an add-on, connecting an account or spending credits.
ReelStack has not independently tested this workflow. Preview one representative shot and check motion, continuity and output quality before applying it to a full production. No result, cost saving or model capability is guaranteed.
How to interpret the numbers
38,591 views captured 23 October 2026. ReelStack recommendation score: 12/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
Show the calculation
- 65% performance: 0.22× current views vs the channel's recent median; same-age history is not yet available. View-evidence factor 97% (views / (views + 1,000))
- 25% freshness: 0/100 with a 14-day half-life
- Momentum pending: collecting daily snapshots; its 20% weight goes to observed performance, not free points
- 10% engagement: 4/100; likes + 4× comments, smoothed with a 500-view neutral prior