GPT Image 2.5 vs Nano Banana 2: compare controls and deliverables

This is a specification and workflow comparison, not a measured image benchmark. The candidates are gpt-image-2.5-flare, gpt-image-2.5-sunburst and nano-banana2. Compare what your application must control before deciding which outputs to evaluate. Contract check date: 2026-11-03; confirm current schemas and access before paid calls.

Compare the platform controls

Control	GPT Image 2.5 Flare / Sunburst	Nano Banana 2
Platform endpoint	POST /v1/tasks	POST /v1/tasks
Reference images	input.images	input.images
Quality selector	low, medium, high, xhigh, max, auto	No quality field in the shared schema
Background selector	auto, transparent, opaque	No background field in the shared schema
Native size input	size, subject to size rules and routing policy	No size field in the shared schema
Shared example tiers	1k, 2k, 4k	1k, 2k, 4k; schema also declares 512
Output formats	PNG, JPEG, WebP	PNG, JPEG, WebP

An undeclared field is not proof that an upstream model can never produce a similar result. It means you cannot assume that control is accepted by this platform route. Do not copy GPT Image’s quality or background into a Nano Banana task. Flare and Sunburst sharing a schema does not prove identical output quality or cost.

Filter candidates by the deliverable

For assets requiring an explicit transparent-background parameter, the GPT Image route exposes that control; validate alpha and edges with the transparent-asset workflow. For a photographic reference-based product card, all three can be candidates if their current contracts accept the required input; use the product-image acceptance brief.

If the delivery system requires exact pixel dimensions, inspect the GPT Image size rules and the actual output. A 1k tier is not a promise of identical dimensions across models. An accepted parameter is not a guarantee that a creative instruction will be followed.

Start with a shared input, then disclose differences

This illustrative task uses only shared input fields. Verify the live schemas before changing model to either GPT Image 2.5 variant. Each submission is a separate paid task; this JSON is not a batch request.

{
  "model": "nano-banana2",
  "input": {
    "prompt": "A single unbranded red ceramic cup on a pale gray studio background. Entire handle visible, centered square composition, soft side lighting, no text and no extra objects.",
    "resolution": "1k",
    "aspect_ratio": "1:1",
    "output_format": "png"
  }
}

For GPT Image, record the effective quality setting even when omitted; the repository default is medium. Nano Banana has no matching quality control here. Thus this is a common-deliverable comparison, not a claim that compute or internal settings are equal. Keep prompt, references, crop intent and reviewer rubric fixed, and record returned dimensions before scoring.

Use a decision sheet instead of a winner label

Blind review can score complete subject, correct object count, crop usability, material fidelity and absence of unwanted text. Mark invalid files and failed tasks separately from creative rejections. Include all charged attempts when calculating cost per accepted deliverable, and report unavailable charge records separately.

For a transparent asset, alpha is a required pass condition; for an opaque product card, it may be irrelevant. Do not average away a missing hard requirement with an attractive background. Choose the least expensive workflow that passes your requirements in your approved sample, then retest when the route or prompt changes. No result or savings percentage is supplied by this article.

Sources and follow-up

Checked 2026-11-03: OpenAI Flare, OpenAI Sunburst, and Google image model identities. The table follows platform repository schemas, not a wholesale copy of native APIs. Continue with Nano Banana 2, Flare, Sunburst and batch costs.

More delivery workflows

• Validate poster lettering separately