GPT Image 2 vs. Nano Banana Pro: How to Choose by Use Case

Where Nano Banana Pro has the clearest public case

Text-heavy layouts, charts and data visuals

If your image contains labels, headings, prices, UI copy, chart annotations or report-style text, Nano Banana Pro has the cleaner official argument. Google says it is best for factual data visualizations that require accurate text rendering and real-world grounding via Google Search .

There are also secondary reviews claiming strong text performance for GPT Image 2, including roughly 99% character-level accuracy in one review and 95%+ multilingual text accuracy in another . Those claims are worth testing, but they are not the same thing as an official cross-model benchmark under identical conditions.

Best first test: Nano Banana Pro for posters, UI mockups, infographics, packaging concepts and charts where broken text makes the output unusable. Keep GPT Image 2 in the test set if your team already works inside OpenAI tooling.

Product mockups, ads and brand assets

Nano Banana Pro is also the better-supported first choice for commercial design work. Google describes it as suited to complex graphic design, high-fidelity product mockups and studio-quality precision .

GPT Image 2 can also be used for image generation and editing, and Fal.ai says its implementation supports generating images from text prompts and editing existing images . But the public OpenAI model page that can be verified here does not present a direct official scorecard for ad quality, product mockups or brand layout performance .

Best first test: Nano Banana Pro for e-commerce product scenes, packaging comps, campaign visuals and polished brand materials. GPT Image 2 remains a sensible parallel test if your production pipeline is already built around OpenAI APIs.

Where GPT Image 2 may fit better

OpenAI-centered production workflows

GPT Image 2 is easiest to justify when the workflow question matters as much as image quality. OpenAI’s model page lists rate limits by tier, including 100,000 TPM / 5 IPM at Tier 1 and 8,000,000 TPM / 250 IPM at Tier 5 . For teams, those limits can matter as much as prompt quality, because they affect batch generation, internal tools, approval workflows and delivery timelines.

TPM means tokens per minute, while IPM means images per minute. If you are building a product or internal creative tool, those limits should be part of the benchmark—not an afterthought.

Best first test: GPT Image 2 when your app, automation or internal tooling already depends on OpenAI infrastructure and you need predictable API integration.

Custom dimensions and provider-specific implementations

Fal.ai’s GPT Image 2 implementation lists custom image dimension rules: both edges must be multiples of 16, the maximum single edge is 3840px, the maximum aspect ratio is 3:1, and total pixel count must be between 655,360 and 8,294,400 pixels . Fal.ai also lists GPT Image 2 pricing from $0.01 per image for low-quality 1024×768 output up to $0.41 per image for high-quality 4K output .

That does not mean every GPT Image 2 route will behave identically. It means teams should compare the exact route they plan to use: OpenAI directly, Fal.ai, Replicate or another provider.

Best first test: GPT Image 2 if custom output dimensions and OpenAI-compatible workflows are central to the job, but verify the limits on the provider you will actually use.

Complex prompts and editing: no universal winner

Nano Banana Pro has strong official language behind it: Google calls it a reasoning-driven engine with advanced creative control for professional-grade editing and generation . That makes it especially relevant for design briefs with multiple constraints—layout, text, product fidelity, brand feel and factual grounding.

GPT Image 2 is described in secondary analysis as promising for complex scene building, UI generation and natural-looking social assets . That is useful context, but it should be read carefully: third-party comparisons often vary in prompts, resolutions, number of generations and how the winning image is selected.

Practical split: try GPT Image 2 for conversational iteration, natural scenes and OpenAI-native editing workflows. Try Nano Banana Pro first when the image is closer to a commercial layout: product, typography, diagrams, brand elements or data visualization.

4K, resolution and output size

This is one of the easiest areas to misunderstand because the answer depends on the access route.

For GPT Image 2, Fal.ai states a maximum single edge of 3840px, a maximum aspect ratio of 3:1 and a total pixel range of 655,360 to 8,294,400 pixels for custom dimensions . Fal.ai’s separate GPT Image 2 page says pricing ranges from $0.01 per image at low-quality 1024×768 to $0.41 per image for high-quality 4K output .

For Nano Banana Pro, public information is more fragmented. OpenRouter lists google/gemini-3-pro-image-preview and provides token pricing for the model . Secondary pricing guides describe 1K–2K images at $0.134 and 4K images at $0.24 . Another guide treats Nano Banana Pro’s maximum native resolution as 4K .

Practical rule: if 4K delivery is mandatory, do not choose from the model name alone. Confirm maximum resolution, aspect ratio, quality settings, file format, retry behavior and price with the exact provider you will use.

Pricing: compare approved-image cost, not sticker price

Pricing is not just a model property. It changes by provider, resolution, quality setting, batch processing, retries and whether you need human cleanup.

OpenAI’s pricing page says the Batch API can save 50% on inputs and outputs for asynchronous work over 24 hours . Fal.ai lists GPT Image 2 at $0.01 per image for low-quality 1024×768 output up to $0.41 per image for high-quality 4K output .

Nano Banana Pro pricing also depends on route. OpenRouter lists token pricing for google/gemini-3-pro-image-preview , while secondary guides describe per-image prices such as $0.134 for 1K–2K and $0.24 for 4K . Those numbers are useful planning inputs, but they should be checked against the current pricing page or contract for your actual provider.

The real metric: cost per usable final image. A cheaper generation is not cheaper if it produces more broken text, layout errors, rejected outputs or manual retouching.

Speed and latency: public evidence is thin

Speed is the hardest category to judge from public sources. Replicate shows a GPT Image 2 run log with one image generated in 38.8 seconds, a predict time of about 40.64 seconds and total time of about 40.66 seconds . That is a useful example, but it is not a benchmark average.

For Google, TechCrunch reported that Nano Banana 2 keeps some high-fidelity characteristics of the Pro model while generating images faster . But that is about Nano Banana 2, not a direct GPT Image 2 vs. Nano Banana Pro speed test.

Practical rule: measure latency yourself. Resolution, quality settings, input images, queue depth, region, provider and concurrency can all change the result.

Recommended first pick by use case

How to run a fair internal benchmark

Use 20 to 50 prompts that look like your real work, not generic showcase prompts. For each model, keep the prompt, aspect ratio, resolution, number of attempts and selection rules consistent.

Score each output on:

• Prompt adherence: subject, composition, camera angle, background and forbidden elements.
• Text accuracy: product names, UI labels, prices, headings and chart labels.
• Layout stability: whether repeated generations keep a usable structure.
• Reference consistency: products, people, colors and brand-like visual elements.
• Editing tolerance: whether small requested changes break the image.
• Resolution and format: required aspect ratios, maximum size, file type and compression.
• Latency: average time, peak-time behavior and concurrent-generation behavior.
• Total cost: cost to reach one approved final image, including retries and manual cleanup.
• Operational fit: rate limits, billing route, logging, review process and commercial usage requirements.

Bottom line

There is no responsible public-source basis for declaring one overall winner. Fal.ai’s own note that an Arena ranking is not an official OpenAI benchmark is a good reminder to treat leaderboards carefully .

Choose Nano Banana Pro first for typography-heavy design, product mockups, brand visuals, diagrams, factual data visualization and Google Search-grounded imagery, because that matches Google’s official positioning .

Choose GPT Image 2 first when your workflow is OpenAI-centered, when API rate limits and integration are central, or when a provider such as Fal.ai gives you the dimension and 4K controls you need .

For any serious production decision, run a side-by-side test with your own prompts, provider route, resolution, quality settings and approval criteria. Public benchmarks can narrow the field. They should not make the final call for you.