Nano Banana 2 vs GPT Image 2: Price, Text, and Resolution

Google's image generation documentation identifies Nano Banana 2 as gemini-3.1-flash-image. The same page now calls it the previous-generation high-efficiency workhorse model and recommends Nano Banana 2.1 for all new projects. That does not make Nano Banana 2 unusable; it means the official default has moved.

OpenAI's documentation identifies GPT Image 2 as gpt-image-2, and its model page lists gpt-image-2-2026-04-21 as the snapshot. The image generation guide now presents gpt-image-2.5-sunburst and gpt-image-2.5-flare as the models for new integrations and places the gpt-image-2 details under earlier models.

A third-party product can use its own display names and billing. Before comparing outputs, record the service, the label it shows, the settings, and the date. If the platform does not disclose the upstream model ID, treat the mapping as unconfirmed.

• Google model ID: gemini-3.1-flash-image.
• OpenAI model ID: gpt-image-2 (snapshot gpt-image-2-2026-04-21).
• Google recommends Nano Banana 2.1 for new projects as of 2026-10-22.

Google prices Nano Banana 2 per image at four output sizes on the standard tier: $0.045 at 0.5K, $0.067 at 1K, $0.101 at 2K, and $0.151 at 4K. The pricing page was last updated 2026-10-19. Its batch tier is roughly half: $0.022, $0.034, $0.050, and $0.076 for the same sizes.

OpenAI prices GPT Image 2 by tokens: $8 per million image input tokens and $30 per million image output tokens on the standard tier, or $4 and $15 on batch. The image generation guide translates that into per-image examples at 1024x1024: $0.006 at low quality and $0.211 at high quality. Other sizes and quality settings land between or beyond those examples, which is why OpenAI publishes a pricing calculator.

Both figures are vendor list prices for developers. Nano Banana Lite sells credits, not vendor tokens, so compare the credit cost shown in the workspace before a job rather than the provider list price.

List prices become easier to read at batch scale. Ten Nano Banana 2 images at 1K cost $0.67 at the standard per-image price; ten at 4K cost $1.51. Ten GPT Image 2 images at 1024x1024 cost $0.06 at low quality and $2.11 at high quality.

That spread, not a single headline price, is the useful signal. If you need many throwaway drafts, GPT Image 2's low tier is the cheapest published option in this comparison. If you need a specific final size and would otherwise upscale, Nano Banana 2's 4K tier is $0.151 per image and its 2K tier is $0.101.

Neither calculation includes platform credits or markup. Check the cost displayed in the workspace before generating, and remember that a rejected image still consumes whatever the platform charges for it.

Nano Banana 2 documents 0.5K, 1K, 2K, and 4K output, with 1K as the default. Its model page also lists added aspect ratios of 1:4, 4:1, 1:8, and 8:1. If your deliverable is a fixed 4K canvas, this is the simpler documented path of the two.

GPT Image 2 recommends 1024x1024, 1536x1024, and 1024x1536, and accepts custom WIDTHxHEIGHT values when both edges are multiples of 16, the aspect ratio stays between 1:3 and 3:1, neither edge exceeds 3840 pixels, and total pixels stay between 655,360 and 8,294,400. Resolutions above 2560x1440 are labeled experimental.

In practice, Nano Banana 2 gives you named resolution tiers while GPT Image 2 gives you a size formula. Choose the one that matches how your downstream layout is specified.

• Nano Banana 2: 0.5K, 1K, 2K, and 4K output tiers, 1K default.
• GPT Image 2: three recommended sizes plus custom sizes under the published constraints.
• GPT Image 2 sizes above 2560x1440 are experimental per the vendor guide.

Google's image generation docs allow up to 10 object images and up to 4 character images for the Nano Banana 2.1 and Gemini 3.1 Flash Image column, 14 references in total. That is a documented ceiling, not a promise that every reference will be honored equally.

OpenAI documents one or more reference images for GPT Image 2 and shows an example with four; the page checked on 2026-10-22 does not state a maximum. If you supply a mask with multiple images, the mask applies to the first image.

For identity-heavy or catalog work where several references define different details, Nano Banana 2's published breakdown is easier to plan around. For a single-reference edit, both are viable, and the result on your own source is the deciding factor.

Google's image generation docs describe legible, stylized text for infographics, menus, diagrams, and marketing assets, and the Nano Banana 2 model page lists improved i18n text rendering. No accuracy benchmark appears on the pages checked.

OpenAI says GPT Image 2 is significantly improved at text but can still struggle with precise text placement and clarity. That is a candid limitation, not a disqualification.

For dense typography, neither model removes the need to proofread. Generate the exact wording, inspect it character by character at final display size, and keep a layout step for text you cannot afford to regenerate. If text is the main job and the budget allows, Nano Banana Pro is Google's higher-control option at $0.134 per 1K or 2K image and $0.24 per 4K image.

Google states that all generated images include a SynthID watermark. Plan for that if your workflow requires an unmarked file, and check the current terms instead of assuming a setting removes it.

OpenAI documents transparent backgrounds for GPT Image 2 by setting background to transparent and using PNG or WebP output. If you need a cutout for a catalog or a design file, that is a documented option.

The OpenAI image generation guide checked on 2026-10-22 does not describe a watermark policy. That is an absence of documentation, not proof of absence; verify the provider terms before publishing.

Write an acceptance line before generating: subject, details that must stay fixed, text that must be readable, output size, and where the image will be used. Then run the same prompt and the same reference through both models, with the same output size where the controls allow it.

Make two attempts per model, not ten. Score each result pass or fail against the acceptance line, then note style preference separately. A model that fails two hard criteria is not rescued by a nicer look.

Record the label, settings, date, and the reason you accepted or rejected each image. That record is the only benchmark you can defend later, and it is more useful than a generic leaderboard.

The decision is less about a permanent winner and more about which documented constraint matches your job. Use the list below as a starting order, then let your own test override it.

• Choose Nano Banana 2 when you need a documented 4K tier, many references, or a named resolution ladder.
• Choose GPT Image 2 when you need cheap low-quality drafts at volume, custom dimensions, or a documented transparent background.
• Choose Nano Banana Pro when dense text and higher control justify $0.134 per 1K or 2K image and $0.24 per 4K image.
• Check Nano Banana 2.1 first for a new Google-model project: Google recommends it and lists 1K at $0.0336, 2K at $0.0504, and 4K at $0.113 per image.
• Stop and change the brief if neither model passes two attempts; switching models rarely fixes an unclear instruction.