GPT Image 2.5 vs GPT Image 2 vs Nano Banana: How to Test

Compare GPT Image 2.5, GPT Image 2, and Nano Banana using the same production brief and separate tests for new images and edits. Select the option that produces usable assets with an acceptable review cost.

This article provides a comparison method. It does not report a benchmark we have not run. Model names and controls also differ between providers and applications, so record the exact option used in each test.

Identify the Exact Models

In ReelsFarm, the GPT Image 2.5 option uses GPT Image 2.5 Sunburst. [OpenAI describes Sunburst](https://developers.openai.com/api/docs/models/gpt-image-2.5-sunburst) as an image generation and editing model suited to editing precision.

[OpenAI's image guide](https://developers.openai.com/api/docs/guides/image-generation) also covers GPT Image 2. [Google introduced Nano Banana](https://developers.googleblog.com/introducing-gemini-2-5-flash-image/) as the name associated with Gemini 2.5 Flash Image. Later Google variants should be identified by their actual model name rather than treated as identical.

Use the models available in your account. An application's exposed options may be narrower than its provider's full API.

Test New Images and Reference Edits Separately

A new-image brief tests composition and prompt following. A reference edit tests preservation as well as the requested change.

For example, generate a creator-style product scene from text, then run a separate test that changes the background of an approved reference. A model can pass one task and need more corrections on the other.

Use a Matched Brief

Try: “Create a vertical lifestyle image for a reusable bottle. Show one person beside a kitchen counter. Keep one bottle visible, leave clear space for a headline, and use soft window light. Do not add claims or extra packaging.”

For the edit test, add: “Keep the reference person's facial features, hairstyle, clothing, and bottle shape. Change only the background to a bright kitchen.”

Give each model the same relevant references and intended layout. If settings cannot match exactly, record the difference rather than calling the trial fully controlled.

Define Pass and Fail Before Reviewing

Choose concrete requirements:

• The subject and scene match the brief.
• The product shape and visible label remain usable.
• Hands and object contact look plausible.
• The edited person remains recognizable.
• The composition supports the intended crop and text.

A failed product detail should not be outweighed by attractive lighting. Mark serious defects as a rejection even if the rest of the frame looks good.

Count Attempts and Review Time

Generate a small initial batch for each task. Record the model, settings, elapsed time, spend, and corrections. Count approved outputs rather than choosing a single impressive sample.

Use total production spend divided by approved assets as one comparison. Also record human review time. A model with a lower request price may need more editing before the asset is usable.

Write a Routing Decision

Your result might be: “Use one model for simple concept images and another for reference-preserving edits.” It should name the tested task and the evidence from your own batch.

Do not replace every existing workflow because a newer model exists. Keep working prompts until a representative trial supports a change.

For edit-specific guidance, read reference editing with GPT Image 2.5.

FAQ

Which model is best for product ads?

There is no result established by this article. Test your real product references and reject distorted packaging or unsupported claims before comparing usable outputs.

Does higher quality guarantee identity consistency?

No. Reference choice, edit instructions, and output review still matter. Use the quality controls exposed by your application.