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There is no universal winner. In two publisher-run comparisons, Google’s Nano Banana 2.1 and OpenAI’s GPT Image 2.5 each did some tasks better. One five-prompt test split its wins across product imagery, text, editing, photography, and character consistency; an eight-prompt API test found accuracy close, with differences in styling, literal instruction-following, and speed.
These were comparisons of named image models—not a controlled test of the Gemini and ChatGPT consumer apps. The distinction matters: “ChatGPT” alone does not identify a particular image model, settings, or generation workflow.
What was compared—and what was not
Google identifies Nano Banana 2.1 as an image-generation and conversational-editing model with the developer ID gemini-nano-banana-2.1. Its documentation describes it as an update to Nano Banana 2, with improvements to image quality, prompt adherence, multi-turn character consistency, and text rendering. Google lists 1K, 2K, and 4K output options. Model names and availability can change, so consult Google’s image-generation documentation for current details.
The two comparisons used different GPT Image 2.5 variants and different environments. Fuser compared Nano Banana 2.1 with GPT Image 2.5 Flare through its nodes, which it said called Google and OpenAI directly. GoEnhance compared Nano Banana 2.1 with GPT Image 2.5 Sunburst through its API. Neither setup should be treated as a direct test of the ChatGPT app against the Gemini app.
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| Comparison | Models and setup | What it can tell you |
|---|---|---|
| Fuser, 6 October 2026 | Nano Banana 2.1 at 2K and GPT Image 2.5 Flare at quality “auto”; five prompts, one generation per prompt, no retries or cherry-picking. PNG output was used. For editing and character tasks, both started from images generated with another model. | A useful, small task-by-task example. One image per task is not enough to establish a general win rate. |
| GoEnhance, 2026 | Nano Banana 2.1 at 1K with its default thinking level and GPT Image 2.5 Sunburst at high quality; eight API prompts. | A separate API comparison covering text, counting, reference edits, photorealism, and a wide landscape. Its settings and results are not interchangeable with Fuser’s. |
Both tests are publisher-run practical comparisons, not large independent benchmarks. Prompts, model variant, settings, number of attempts, reference images, output size, and judging criteria can all change what a comparison shows.
How the models compared by task
Photoreal scenes
In Fuser’s single photoreal-scene prompt, GPT Image 2.5 Flare won narrowly. Fuser preferred its softer background blur and more focused hands; Nano Banana’s background remained comparatively sharp. That is one judged result, not proof that GPT Image is better at every photographic prompt.
Posters and text inside images
Fuser called the poster task a tie: both models rendered all requested lines correctly, though their design treatments differed. GoEnhance likewise reported that both models spelled requested menu and infographic text correctly in its examples. It also observed that GPT Image sometimes added unrequested text, while Nano Banana more often stayed within the specified content.
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So a correct-spelling result does not settle whether an image is usable. Check the wording, placement, hierarchy, legibility, and any extra copy against the prompt. Both publishers tested examples rather than every typeface, language, or layout.
Product imagery and preserving details
Fuser preferred Nano Banana 2.1 for a product shot, judging that it better met the request for a board, lighting, shadow, and copy space. In a separate background-edit task, GPT Image 2.5 Flare narrowly won because it preserved the bottle, cap, and label more faithfully; Fuser said Nano Banana restyled the cap collar. The outcomes point to a practical distinction: a model may handle a fresh composition well but alter a small product feature during an edit.
Character consistency and reference edits
For a prompt asking for the same person in a new scene, Fuser judged GPT Image 2.5 Flare better at retaining facial features and producing readable signs. In GoEnhance’s reference-edit examples, both models kept recognizable subjects, but differed in framing and invented detail. GoEnhance’s eight-prompt set also included a counting task, which both models passed.
Style, speed, and cost in GoEnhance’s setup
GoEnhance described the two models as close on accuracy: GPT Image produced richer or more polished styling in some examples, while Nano Banana followed the specified content more literally. In that publisher’s API environment, median generation time at high quality across eight prompts was 18.9 seconds for Nano Banana 2.1 (range 15–22 seconds) and 102.8 seconds for GPT Image 2.5 Sunburst (range 61–111 seconds). The figures include queue time and are not general latency guarantees.
GoEnhance also reported its own token costs, with one token priced at $0.02 in its 2026 article. These are service-specific figures—not official Google or OpenAI prices:
| Output size | Nano Banana 2.1 | GPT Image 2.5 |
|---|---|---|
| 1K, high quality | 2.92 tokens | 2.84 tokens |
| 2K | 4.02 tokens | 5.78 tokens |
| 4K | 8.08 tokens | 9.61 tokens |
The 1K comparison is explicitly listed at high quality; the source’s table does not separately qualify the 2K and 4K rows as high quality. These numbers should not be used to estimate what generation costs through Google or OpenAI directly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What Google’s benchmark figures add
Google DeepMind’s 2026 model card reports its own evaluations, including public benchmarks and internal checks for text-to-image, editing, multi-turn work, and regressions. It describes side-by-side human evaluation producing Elo scores for dimensions such as overall preference and design quality, plus a single-sided AutoRater for factuality. These figures provide vendor-reported context; they are not an independent head-to-head against OpenAI.
| Evaluation reported by Google DeepMind | Nano Banana 2.1 with Thinking | Other reported results |
|---|---|---|
| Overall Preference | 1050 ± 14 | Nano Banana 2.1 without Thinking: 1015 ± 13; Gemini 3.1 Flash Image (“Nano Banana 2,” Thinking): 990 ± 7 |
| Infographic Design | 1048 ± 17 | Nano Banana 2.1 without Thinking: 1001 ± 17; Nano Banana 2: 961 ± 12 |
| Infographic Factuality | 0.521 | Nano Banana 2.1 without Thinking: 0.328; Nano Banana 2: 0.179; Nano Banana Pro: 0.265 |
| Multi-Character Consistency | 1106 ± 14 | Nano Banana 2.1 without Thinking: 1068 ± 14; Nano Banana 2: 978 ± 10; Nano Banana Pro: 1011 ± 10 |
| General Editing | 1026 ± 12 | Nano Banana 2.1 without Thinking: 980 ± 15; Nano Banana 2: 938 ± 11; Nano Banana Pro: 939 ± 10 |
The comparison set varies by evaluation, and these numbers do not answer which model will do better on an individual prompt—or how it compares with GPT Image 2.5. Read them as Google’s reported results within its evaluation, not as a shared cross-company scorecard. See the Google DeepMind model card for its stated methodology and model context.
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Which should you choose?
- For a product shot or a tightly specified composition: Nano Banana 2.1 won Fuser’s one product-shot prompt. Treat that as a reason to try it, not a guarantee.
- For keeping small details intact in an edit: GPT Image 2.5 Flare preserved the bottle details better in Fuser’s example. Inspect labels, closures, and other important features closely whichever model you use.
- For posters or menus with exact copy: both publishers saw correct requested text in their examples. Review the complete rendered image for layout and unrequested text before using it.
- For a consistent person across scenes: GPT Image 2.5 Flare was Fuser’s judged winner in one task, while Google reports strong multi-character consistency results in its own model card. These evaluate different things and are not directly comparable.
- For speed or API expense: GoEnhance’s results may help if you use that service under comparable conditions. They do not establish direct-provider pricing or response times.
For a meaningful choice in your own workflow, generate the same prompt with the same reference image and comparable output settings, then judge multiple attempts on the details you actually need. A consumer app session is a separate comparison from a named API model: record the app, model selection, settings, and date if you want results others can reproduce.
Where Nano Banana 2.1 is available
Google DeepMind lists access through the Gemini app, Google AI Studio, Gemini API, Google Search AI Mode, Google Ads, Google Flow, and Google Stitch. It says no special hardware or software is required. Availability, account requirements, and features can vary by product and region; consult Google DeepMind’s current model information and the Gemini API image-generation documentation for the route you plan to use.
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