GPT Image 2 vs Nano Banana Pro: choosing an image model
GPT Image 2 and Nano Banana Pro win different jobs: on-image text, 4K, consistency, or stack fit. How we choose.
This translation was produced automatically using AI. The German version is the editorially reviewed original.
We run both GPT Image 2 and Nano Banana Pro in production for client visuals, and neither is the universal winner. They lead on different jobs. Nano Banana Pro, which is Google's Gemini 3 Pro Image, is the one to reach for when an image carries a lot of text, needs 4K, or has to keep a product or person consistent across a set. GPT Image 2, OpenAI's current image model, holds the top of the general text-to-image leaderboards and fits cleanly if you already live in the OpenAI stack.
So the choice is not about which model is better in the abstract. It is about the job in front of you and the stack you already run.
01. Get the lineage right first
Half the confusion in this comparison is naming, so it is worth fixing before anything else.
🔸 GPT Image 2 is OpenAI's current image model, the successor in the gpt-image line, released in 2026. The older API id gpt-image-1 is the first generation, not this one.
🔸 Nano Banana Pro is Google's marketing name for Gemini 3 Pro Image, announced in November 2025 and built on Gemini 3 Pro.
🔸 Nano Banana without the Pro is the earlier Gemini 2.5 Flash Image, now positioned as the fast and cheap tier. Do not conflate the two, since their output quality and price are different.
Getting this right matters for the same reason model lineage mattered in the Fable 5 sovereignty lesson: you cannot reason about a tool you have misidentified.
02. The comparison that decides real work
The axes that actually change the output you ship:
| Axis | GPT Image 2 | Nano Banana Pro |
|---|---|---|
| On-image text | Strong, a clear step up | Best-in-class, long legible text |
| Prompt adherence | High | High, with Gemini 3 reasoning |
| Multi-image consistency | Reference edits, no stated cap | Up to 14 references, up to 5 people |
| Max resolution | Around 1536px on the long side | 2K and 4K |
| Aspect ratios | Three native ratios | A broader set |
| Search grounding | No | Yes, can pull real-time facts |
| Watermarking | C2PA metadata | SynthID invisible watermark |
| Pricing | Token-based, image output billed per token | Per image, see vendor calculator |
| Stack fit | Native to OpenAI | Native to Google and Vertex |
The short read: Nano Banana Pro wins on text, resolution, and consistency. GPT Image 2 wins on general look and on fitting an existing OpenAI workflow.
03. Where each one wins
🔸 Marketing creative with heavy on-image text or infographics. Nano Banana Pro. Best text rendering, 2K and 4K output, and Search grounding for accurate facts and logos. GPT Image 2 is a fine fallback if you already build on OpenAI.
🔸 Product or character consistency across a set. Nano Banana Pro. It is the one with a stated spec, 14 reference images and 5 consistent people, which is what a coherent campaign or a recurring product shot needs.
🔸 General hero or editorial imagery. Close. GPT Image 2 currently leads the general text-to-image leaderboards, so pick on look preference. Choose Nano Banana Pro if you need 4K straight out of the model.
🔸 Tight stack integration. Use what you already run. An OpenAI shop gets one API and batch discounts with GPT Image 2. A Google or Vertex shop gets native generation with Nano Banana Pro.
This is the same selection discipline we apply to AI video models and to image-to-3D with TRELLIS. Pick by the job, not the logo.
04. Honest limits on both sides
Neither model is free of trade-offs, and the trade-offs are what bite in production.
🔸 GPT Image 2 caps near 1536px on the long side and offers only three aspect ratios, so it loses on large-format and unusual shapes. Token pricing makes edit-heavy work expensive, since edits bill image-input tokens too. Watermarking is C2PA metadata rather than an embedded invisible mark, which is worth confirming for your compliance needs.
🔸 Nano Banana Pro carries the SynthID invisible watermark on output, which is non-removable below the top enterprise tier. Some clients want it gone, so check this early. It also runs at higher cost and latency than the base Nano Banana, and Google does not publish a clean per-image price, so plan cost through the Vertex calculator.
None of this is a deal-breaker. It is the kind of detail that decides which model fits a specific client constraint.
05. How we choose at iiterate
Our default is not loyalty to one model, it is a short checklist. Does the image carry text or need 4K? Does it have to stay consistent across a set? Which cloud does the client already run? Are there watermark or residency constraints?
Most of the time those four questions answer themselves. Text-heavy, high-resolution, or consistency-critical work goes to Nano Banana Pro. General editorial work and OpenAI-native pipelines go to GPT Image 2. We keep both in the toolkit precisely because the right answer changes per brief.
The models will keep leapfrogging each other, so re-check the leaderboards and the pricing at decision time. What stays stable is the habit: match the model to the job, and to the stack the client already lives in.
