Comparing GPT Image 2.5 with Gemini is not a two-model question. OpenAI ships one generation split into two API variants. Google ships a line of five image models whose own API page names the one new projects should use. Choose the wrong Gemini model and the comparison describes a product nobody is shipping.
Everything below comes from the two vendors’ own pages. There are no measured scores and no per-image prices, because neither vendor publishes the numbers that would make those claims checkable.
Align the versions before comparing
Most pages ranking for this query reach for a verdict first. Alignment is the better opening move.
- GPT Image 2.5 is the current OpenAI generation, released on 8 September 2026, and it ships as
GPT-Image-2.5 FlareandGPT-Image-2.5 Sunburst - Google’s image page documents five models under the Nano Banana name and states that new projects should use Nano Banana 2.1,
gemini-nano-banana-2.1 - Pages titled “GPT Image 2 vs Gemini 3.1 Pro” compare a previous OpenAI generation against a Gemini model that Google now lists beneath a newer default
A claim about GPT Image 2 is not a claim about GPT Image 2.5. The narrow head-to-head lives in a separate comparison; this page stays one level up, at the level of the two product lines.
Two vendors, two different lineup shapes
What OpenAI ships
Two variants sharing one parameter set. GPT-Image-2.5 Flare is documented as “the default choice for most applications” and is sold on speed. GPT-Image-2.5 Sunburst is “built for premium visual workflows that benefit from tighter control across edits”. Switching between them changes the model name, not the call. The capability overview works through that split in detail.
What Google ships
Five, each with migration advice attached. Nano Banana 2.1 is the primary high-efficiency workhorse; 2 Lite the fastest and cheapest; 2 the previous-generation workhorse, with a note that new projects should move to 2.1; Pro the premium tier for complex tasks; and the original Nano Banana, on gemini-2.5-flash-image, legacy.
| Dimension | OpenAI documents | Google documents |
|---|---|---|
| Current models | GPT-Image-2.5 Flare, Sunburst | Nano Banana 2.1, 2 Lite, 2, Pro, legacy |
| Stated default | Flare, “for most applications” | 2.1, “all new projects” |
| Reference images | Not published on the pages cited here | “Mix up to 14 reference images” |
| Output sizes | Six quality tiers, custom sizes | 1K, 2K and 4K resolutions |
| Aspect ratio | “Any aspect ratio” | Ten named ratios, listed per model |
| Provenance marking | C2PA metadata and invisible watermarking | “All generated images include a SynthID watermark” |
| Availability | ChatGPT, ChatGPT Work, Codex, and the API | The Gemini API, with a separate app surface |
Editing: a conversation or a canvas
OpenAI documents editing as something you say. The help centre gives two routes: select part of the image with the selection tool and describe the change in chat, or skip the tool and describe the edit in the conversation panel. It states the mechanism — “better at editing only what you’ve asked for” — and then a limit rather than a promise: “Highlights are not always precise, and edits may extend beyond the area you selected.” The editing guide works through the loop.
Google documents editing as a capability list. Its image page frames the whole line as conversational, then attaches multi-turn consistency and reference mixing to specific models — and documents the opposite for one of them: Nano Banana 2 Lite is “not optimized for multiple reference inputs or multi-turn sequential editing”.
Control surfaces each side documents
- Instruction following. OpenAI says generation is “more likely to retain the requested visual direction, composition, and individual details, instead of drifting as instructions become more specific”.
- Composition. OpenAI documents an aspect-ratio picker and lets the ratio be stated in the prompt. Google publishes a fixed ratio list per model and documents “Grounding with Google Search”.
- Text in the image. Google describes “advanced text rendering” for infographics and menus, and lists the languages it performs best in. OpenAI documents adding text and detail, and its guidance still treats precise typography as something to proofread.
- Reference inputs. Google publishes a number, up to fourteen. OpenAI’s pages cited here publish no count at all.
What neither vendor publishes
This is the part most comparisons fill with confident numbers.
- No per-image price. OpenAI’s release links to a separate pricing page; Google’s image page points elsewhere for cost.
- No quota or rate-limit figure for the models compared here.
- No benchmark putting the two lineups against each other, and no quality score for either.
- No accuracy rate for text inside images, on either side.
Run the comparison on your own brief
Because the official record is thin on numbers, compare the documented parts on a brief you control.
Freeze the brief
Give both sides the same words, and do not rewrite them per model.
A weekend market poster.
Headline legible at thumbnail size; palette limited to two colours.
Leave the bottom third clear for opening hours.
Square 1:1 frame, no logos, no watermark, no people.Read the result against your own checklist
Ask whether the drawn text is readable, whether the frame survived, how many turns each side needed to land an edit, and whether anything already right broke along the way. Those four questions map onto things both vendors document, so the answers are comparable. A ranking based on which picture you like more is not.
Frequently asked questions
Which Gemini model should I compare?
Use the one Google names as the default for new projects, Nano Banana 2.1. Comparing a current OpenAI generation against a model Google itself now lists as previous-generation is the most common error in this query.
Which is better at text in images?
Neither vendor publishes an accuracy rate, so no documented answer exists. Google documents the capability and the recommended languages; OpenAI documents adding text and detail. Judge it on your own lettering, and proofread anything that ships.
Can I use the same prompt in both?
You can send the same text, but the two document different control surfaces — a described conversation on one side, a model line with published sizes and a reference-image budget on the other. Keep the brief identical and expect to adapt the wording.
Do either of them mark the output?
Both mark provenance. OpenAI’s release describes C2PA metadata and invisible watermarking; Google states unconditionally that “all generated images include a SynthID watermark”. What that means for commercial use is a terms question, covered in the commercial use guide.
What does each one cost?
Neither page cited here publishes a per-image price, so treat any figure you see quoted as someone else’s arithmetic. Start with what each vendor publishes about access, then follow the pricing links from the vendors themselves.