GPT Image 2 vs Nano Banana Pro: which image model should you test first?

Quick answer

• Start with GPT Image 2 if you mainly need general image generation quality, public leaderboard strength and a clearer way to estimate API costs .
• Start with Nano Banana Pro if your priority is infographics, educational explainers, Gemini integration or image workflows that may benefit from Google Search grounding .
• Do not call editing settled. The cited editing leaderboard separates GPT Image 2 high and Nano Banana Pro by only one Elo point .
• Run your own prompt set before committing if you need reliable text inside images, non-English copy, brand consistency, product fidelity or predictable production behavior.

First, get the names straight

GPT Image 2 is listed in OpenAI’s API documentation as the gpt-image-2 model. The same documentation also refers to snapshot and alias handling, including gpt-image-2-2026-04-21, which matters if a team wants version stability in a production workflow .

Nano Banana Pro is the brand name for Gemini 3 Pro Image. Google says the original Nano Banana name stuck around Gemini 2.5 Flash Image, and that Gemini 3 Pro Image received the upgraded brand name Nano Banana Pro . Google’s product announcement describes Nano Banana Pro as an image generation and editing model built on Gemini 3 Pro .

At a glance

Generation quality: the public leaderboard signal favors GPT Image 2

For standard prompt-to-image work, GPT Image 2 has the clearer public benchmark lead in the sources cited here. Artificial Analysis lists GPT Image 2 high as the top text-to-image model with an Elo score of 1,331 . Arena.ai also lists gpt-image-2 (medium) at No. 1 with a score of 1,512±8, marked Preliminary .

On the same Arena.ai leaderboard, Nano Banana Pro entries are lower: gemini-3-pro-image-preview-2k (nano-banana-pro) is shown at 1,244±4, while gemini-3-pro-image-preview (nano-banana-pro) is shown at 1,232±5 .

That is a meaningful signal, but it is not the same as a procurement test. A leaderboard does not guarantee success on your exact use case: product pack shots, campaign banners, interface mockups, packaging labels and brand-controlled creative can fail in different ways. If the output will ship to customers, test the models on your actual prompts and reference assets.

Editing: a one-point gap is not a decision

The editing picture is much less clear. Artificial Analysis lists GPT Image 1.5 high as the leading image editing model at 1,267 Elo, followed by GPT Image 2 high at 1,251 and Nano Banana Pro at 1,250 .

That one-point difference between GPT Image 2 high and Nano Banana Pro is not enough to declare a practical winner. For editing tasks—retouching a product shot, preserving a character, changing a background, updating a branded layout or modifying only part of an image—the better test is simple: give both models the same source image, the same prompt and the same success criteria.

Text inside images is not the same as text-to-image ranking

A high text-to-image ranking does not automatically prove that a model will render letters, labels or long copy more accurately inside an image. The cited leaderboards rank text-to-image generation, not a standalone typo, dense-copy or multilingual text-rendering benchmark .

Nano Banana Pro has a more explicit official pitch in this area. Google says Nano Banana Pro uses Gemini 3’s reasoning and real-world knowledge to visualize information, with examples such as city weather, how to make Elaichi Chai and house plant care . Google’s developer post also says Gemini 3 Pro Image offers higher text rendering accuracy, robust world knowledge and the ability to use Google Search grounding based on the user’s prompt .

So the practical split is this: GPT Image 2 has the stronger general generation leaderboard signal, but Nano Banana Pro should be tested separately for infographics, educational explainers and information-heavy visuals .

Pricing: GPT Image 2 is easier to budget

OpenAI publishes example prices for GPT Image 2 by quality level and output size. For 1024×1024 output, the listed examples are low $0.006, medium $0.053 and high $0.211. For 1024×1536 and 1536×1024 output, the listed examples are low $0.005, medium $0.041 and high $0.165 .

Those examples should not be treated as a guaranteed final bill for every workflow. Still, they give teams a concrete baseline for planning large batches, A/B creative tests or API-driven image generation.

Google’s cited developer materials describe Nano Banana Pro’s paid preview through the Gemini API, Google AI Studio and Vertex AI, along with capabilities such as Search grounding . But within the Google official materials cited here, there is no directly comparable quality-by-resolution price table like OpenAI’s .

Resolution claims: do not mix up Nano Banana models

For GPT Image 2, OpenAI’s pricing table explicitly shows 1024×1024, 1024×1536 and 1536×1024 examples, and notes that additional sizes are available .

For Google’s Nano Banana family, the names can be easy to blur. TechCrunch’s report that images can be created from 512px to 4K refers to Nano Banana 2, not Nano Banana Pro . That report should not be used as proof of Nano Banana Pro’s maximum output resolution.

Which model should you try first?

Test GPT Image 2 first if…

• Public text-to-image leaderboard performance is a major selection criterion .
• You are comparing general-purpose creative output for ads, product visuals, posters or campaign concepts.
• You need a clearer starting point for cost estimates by quality and output size .
• You want to use OpenAI’s documented model and snapshot workflow for gpt-image-2 .

Test Nano Banana Pro first if…

• Your brief is an infographic, educational diagram, explainer visual or knowledge-heavy graphic .
• You want to build around Gemini API, Google AI Studio or Vertex AI .
• Search grounding is relevant to the image prompt or product experience .
• You are evaluating image generation and editing together, and want to test the model Google positions for both .

Run a head-to-head test if…

• The image contains non-English copy, long text, labels, tables or small print.
• You need the same character, product or brand asset to stay consistent across many outputs.
• Your production requirements depend on latency, retry rates, policy refusals or final billed cost.
• The work involves client-facing creative where a near miss is still a failure.

Bottom line

If you are starting from scratch and need the strongest public signal for general text-to-image generation plus easier budget planning, GPT Image 2 is the more rational first test .

If the job is information visualization, an explainer graphic, a Gemini-native workflow or a Search-grounded creative tool, Nano Banana Pro has a strong case for going first because that is where Google’s own positioning is most direct .

For editing, the most honest answer is not GPT Image 2 or Nano Banana Pro. It is: test both. The public editing scores are essentially neck and neck .