The Tool Desk
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What was compared—and what “ChatGPT” means here
Nano Banana 2.1 is Google’s image-generation and conversational-editing model, identified in Google’s developer documentation as gemini-nano-banana-2.1. The comparison reports did not test an unspecified “ChatGPT” experience: Fuser compared it with gpt-image-2.5-flare, while GoEnhance used GPT Image 2.5 Sunburst through its API. Those model variants and third-party setups should not be treated as identical to whatever image-generation model or defaults a user currently sees in ChatGPT.
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Google describes Nano Banana 2.1 as an update to Nano Banana 2, with improvements to image quality, prompt adherence, multi-turn character consistency and text rendering. Its documentation lists 1K, 2K and 4K output options and names Nano Banana 2 Lite, Nano Banana 2, Nano Banana Pro and the legacy Nano Banana model as other family choices. Model names, features and availability can change; consult Google’s image-generation documentation for current details.
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How the head-to-head tests came out
Fuser: five tasks, one generation per model
Fuser says it ran five prompts on 6 October 2026, comparing Nano Banana 2.1 with GPT Image 2.5 Flare through Fuser nodes it says call Google and OpenAI directly. It used each node’s defaults except for PNG output; Nano Banana produced 2K images, while GPT Image used quality set to “auto.” The character and editing tasks started from images made with another model. Fuser says there were no retries or cherry-picked outputs. Each task had only one generation per model, so these results are useful examples, not a statistical verdict. The full report is at Fuser’s comparison.
| Task | Fuser’s reported result |
|---|---|
| Photoreal scene | GPT Image 2.5 Flare won narrowly: Fuser preferred its softer background blur and focused hands. Nano Banana’s background was fairly sharp. |
| Poster text | Tie on requested wording: both got all lines right, but their design treatments differed. |
| Product shot | Nano Banana 2.1 won for the requested board, lighting, shadow and copy space. |
| Background edit | GPT Image 2.5 Flare won narrowly for preserving the bottle, cap and label; Nano Banana restyled the cap collar. |
| Same person in a new scene | GPT Image 2.5 Flare won; Fuser judged it better at preserving features and producing readable signs. |
GoEnhance: eight API prompts
GoEnhance compared Nano Banana 2.1 with GPT Image 2.5 Sunburst using eight prompts and 1K outputs. It used Nano Banana’s default thinking level and set GPT Image quality to high, which it considered similar in cost. The prompts covered text rendering, counting, reference-image edits, photorealism and a wide landscape. Its results and figures reflect its API service and evaluation, not a universal ranking; see GoEnhance’s report.
In the examples it described, both models rendered requested menu and infographic text correctly and both passed a counting task. GoEnhance found GPT Image’s designs more polished in some cases, but said it sometimes added text that had not been requested. Nano Banana more often stayed within the specified content. Both preserved recognizable subjects in reference edits, although framing and invented details differed. GoEnhance’s overall characterization was close accuracy, with GPT Image tending toward richer styling and Nano Banana toward more literal instruction-following.
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- Product composition: Fuser’s single product-shot comparison favored Nano Banana for placement, lighting, shadow and open copy space. That is one prompt, not proof it will win other product briefs.
- Photoreal scenes and people: Fuser favored GPT Image narrowly for blur and focused hands, and also for maintaining a person’s features in a new scene. These judgments came from single outputs.
- Exact text: Neither report supports a blanket claim that one always spells text better. Fuser’s poster task was a tie; GoEnhance said both got requested menu and infographic wording right. Correct spelling does not guarantee the desired layout or prevent extra text.
- Reference-image editing: Fuser preferred GPT Image for preserving a bottle’s details, while GoEnhance found both models kept subjects recognizable but differed in framing and added details. If exact labels, caps or other product features matter, inspect the result closely.
- Literal prompt adherence versus polish: GoEnhance observed that Nano Banana more often stayed within specified content, while GPT Image sometimes produced richer styling and unrequested text. Treat that as an observation from its eight prompts, not a fixed personality of either model.
Speed and cost depend on the service
GoEnhance reported a median generation time of 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) across its eight high-quality prompts. The times included queue time in GoEnhance’s API environment; they are not a guarantee about other services or current app response times.
Its own token table listed the following API-service costs. GoEnhance priced one token at $0.02 in the report. These are GoEnhance figures, not official Google or OpenAI API prices, and do not establish which provider is cheaper outside that service.
| Output size | Nano Banana 2.1 | GPT Image 2.5, high quality |
|---|---|---|
| 1K | 2.92 tokens | 2.84 tokens |
| 2K | 4.02 tokens | 5.78 tokens |
| 4K | 8.08 tokens | 9.61 tokens |
What Google’s benchmark data does—and doesn’t—show
Google DeepMind’s 2026 model card reports benchmark and internal evaluations for image generation, editing, multi-turn work and regression checks. Its methodology includes 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 are vendor-reported results and compare Google models in the card’s evaluation; they are not an independent Nano Banana-versus-OpenAI test.
| Evaluation in Google DeepMind’s 2026 card | Nano Banana 2.1 with Thinking | Nano Banana 2.1 without Thinking | Other Google-model result reported |
|---|---|---|---|
| Overall Preference (Elo) | 1050 ± 14 | 1015 ± 13 | Gemini 3.1 Flash Image (“Nano Banana 2,” Thinking): 990 ± 7 |
| Infographic Design (Elo) | 1048 ± 17 | 1001 ± 17 | Nano Banana 2: 961 ± 12 |
| Infographic Factuality | 0.521 | 0.328 | Nano Banana 2: 0.179; Nano Banana Pro: 0.265 |
| Multi-Character Consistency (Elo) | 1106 ± 14 | 1068 ± 14 | Nano Banana 2: 978 ± 10; Nano Banana Pro: 1011 ± 10 |
| General Editing (Elo) | 1026 ± 12 | 980 ± 15 | Nano Banana 2: 938 ± 11; Nano Banana Pro: 939 ± 10 |
The card’s figures provide context for Google’s own evaluations, not a cross-company scorecard. They cannot settle which model will do better on a particular prompt or in a consumer app.
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How to make a fair comparison for your own work
- Identify the exact model and interface. Record the model name or ID, whether you used an app or API, and the service supplying it. “ChatGPT” alone does not identify the model or settings.
- Use the same prompt and inputs. Keep wording and reference images identical, especially for editing tasks. State what must remain unchanged, such as a label, face or object shape.
- Match output conditions as closely as possible. Note resolution, quality, thinking mode and other available controls. If the services do not offer matching settings, document the difference rather than calling the comparison controlled.
- Generate more than one result when the decision matters. A single output can be unusually strong or weak. Compare several generations per prompt and keep every attempt, rather than selecting only favorable examples.
- Score the requirement that matters. For an infographic, check factual content and spelling separately from layout; for a product image, inspect geometry, labels and lighting; for an edit, check preservation of the reference. Record failures and unwanted additions as well as strengths.
- Measure time and price in the actual service you will use. Include queue time if relevant, confirm current billing and output size, and avoid transferring a third-party service’s figures to a different app or API.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
