OpenAI JUST Dropped GPT Image 2 and It’s WILD (Nano Banana Pro Killer?)
GPT Image 2 introduces powerful native multi-angle and multi-character generation capabilities that directly challenge existing industry benchmarks.
What this lesson covers
This video puts OpenAI's GPT Image 2 through a comprehensive head-to-head comparison against Google's Nano Banana Pro across nearly 50 prompts. It tests advanced capabilities like multi-character consistency, perspective switching, and multi-image generation.
Key takeaways from the creator
AI-extracted notes, not independently verified product claims. Timestamp links let you check each point in the original video.
- 00:00 ↗
OpenAI's GPT Image 2 originally surfaced on the AI image arena under the hidden name 'duct tape' before global rollout.
- 01:01 ↗
The model features advanced character consistency, multi-angle generation, and self-correcting thinking capabilities.
- 02:07 ↗
In single character reference tests, GPT Image 2 maintained intricate details like tattoos and piercings better than Nano Banana Pro.
- 03:04 ↗
GPT Image 2 successfully maintained consistency across six distinct character references in a single prompt test.
- 06:27 ↗
The model can generate multiple consecutive cinematic photos or grid panels with a single prompt while maintaining character continuity.
Workflow outlined in the video
- Access GPT Image 2 through ChatGPT as it rolls out globally.
- Test character consistency by uploading reference photos with specific details like tattoos or piercings.
- Experiment with multi-character prompts to evaluate group consistency across models.
- Use multi-image prompt generation commands to create sequential storyboard grids.
- Combine image outputs with video generation models like Seedance 2.0 to animate your scenes.
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.
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How to interpret the numbers
17,336 views captured 22 October 2026. ReelStack recommendation score: 41/100. Formula: radar-v5. This is ReelStack’s calculation, not a YouTube rating or a measure of factual accuracy.
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- 65% performance: 1.19× views vs 5 other channel videos observed at a similar age. View-evidence factor 95% (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: 78/100; likes + 4× comments, smoothed with a 500-view neutral prior