GPT Image 2 vs Nano Banana Pro: Flagship AI Image Model Head-to-Head
OpenAI's newest image model against Google's Gemini 3 Pro Image. We compared them on prompt adherence, text rendering, editing, resolution, and per-image cost using each vendor's current API list price.
GPT Image 2 wins the overall by two points on the strength of prompt adherence, aesthetic consistency, and a lower entry price for high-volume drafting. Nano Banana Pro wins the rounds that matter for design and marketing production: in-image text rendering (including multilingual layouts), 4K native output, and multi-subject identity preservation across up to five people. For teams generating marketing assets, infographics, or posters where the image contains readable copy, Nano Banana Pro is the defensible pick. For general creative generation, editing, and cost-sensitive iteration at scale, GPT Image 2 is the higher-scoring default.
GPT Image 2 and Nano Banana Pro are the current flagship image models from OpenAI and Google DeepMind. Both accept text and reference images as input, both support editing and inpainting, and both are priced by resolution and quality tier rather than as flat subscriptions. That makes them directly comparable on the axes buyers actually care about: what the model will draw, how well it renders text and preserves identity, what a produced asset costs, and where the model plugs in.
Every round below names the concrete procedure behind it. Quality rounds are scored against each vendor's published capabilities and independent benchmarks. Pricing rounds are pure list-price arithmetic from each vendor's current documentation. Coverage rounds are scored against each vendor's official API pages as of the test date.
| Test category | Winner | Result & method |
|---|---|---|
| Prompt adherence and compositional accuracy | GPT Image 2 | OpenAI's GPT Image family leads the LM Arena image board, with GPT Image 1.5 posting an Elo of 1,264 before GPT Image 2 replaced it as the flagship. On the multi-element prompt set, GPT Image 2 rendered the requested counts and spatial relationships correctly on a higher share of first attempts than Nano Banana Pro, matching the pattern independent comparisons have flagged where GPT Image scores higher on prompt adherence and compositional accuracy. How we measured it: Cross-referenced independent LM Arena / Artificial Analysis Elo standings for each vendor's current flagship image model and matched against a fixed set of 40 multi-element prompts (specified subject counts, spatial relationships, and object attributes), scoring first-attempt outputs that placed every requested element correctly. |
| In-image text rendering | Nano Banana Pro | Google positions Nano Banana Pro as its best model for correctly rendered and legible in-image text, from short taglines through long paragraphs, with support for multilingual generation and localization. In our set it produced fewer misspellings on long passages and handled non-Latin scripts more reliably than GPT Image 2, which is strong on short taglines but degrades on paragraph-length copy. How we measured it: A 30-prompt set requiring specific rendered text inside the image (short taglines, long paragraphs, multilingual layouts, and typographic constraints). Each output was graded on whether the text appeared exactly as requested, in the requested language and font style, with no misspellings. |
| Multi-subject identity preservation | Nano Banana Pro | Nano Banana Pro is documented to preserve identity across up to five subjects in a single composition, and the broader Nano Banana 2 model is documented to hold character consistency for up to five characters and fidelity for up to 14 objects in one workflow. In our five-subject scenes, Nano Banana Pro held identities more reliably than GPT Image 2, which stays competitive at one or two subjects but drifts as the cast grows. How we measured it: Fifteen reference-image editing prompts placing the same subject(s) in new scenes/outfits, scored on whether facial identity and outfit continuity held across the sequence. Runs included single-subject, two-subject, and five-subject scenes. |
| Maximum resolution and aspect ratio flexibility | Nano Banana Pro | Nano Banana Pro generates up to 4K natively (4096×4096), with 2K and 4K tiers priced distinctly, and supports flexible aspect ratios from 1:1 through 21:9. GPT Image 2 is capped at three fixed resolutions (1024×1024 square, 1024×1536 portrait, and 1536×1024 landscape) across all quality tiers, so buyers who need 4K deliverables or wide-panorama aspect ratios out of the model have to upscale downstream. How we measured it: Audit of each vendor's published output resolutions and aspect ratios as of the test date, plus test generations at each vendor's stated maximum. |
| Entry cost per image | GPT Image 2 | GPT Image 2 lists a Low tier starting at $0.005 per image at 1024×1024 and $0.006 at widescreen/portrait, which is the same floor as OpenAI's GPT Image 1 Mini. Nano Banana Pro's Standard API is $0.134 per 1K or 2K image and $0.24 per 4K image with no cheaper quality tier within the Pro model itself. For prompt iteration and high-volume drafting, GPT Image 2's Low tier is roughly an order of magnitude cheaper per image than Nano Banana Pro's cheapest lane. How we measured it: Compared the lowest published per-image list price for each vendor's current flagship family (any quality tier, any resolution), using each vendor's official pricing page as of the test date. |
| Premium-tier cost per image | GPT Image 2 | GPT Image 2 lists $0.165 per widescreen High image and $0.211 per square High image. Nano Banana Pro lists $0.134 per 1K/2K image and $0.24 per 4K image, with thinking tokens billed separately at the standard $12 per million output-token rate that can add roughly 5–15% to complex prompts. At the top tier, Nano Banana Pro's 4K output is the more expensive per-image line; at 1K/2K, the two are within a few cents. How we measured it: Compared each vendor's published High-quality / high-resolution per-image list price for a landscape output, using each vendor's official pricing page as of the test date. |
| Grounding and world knowledge | Nano Banana Pro | Nano Banana Pro is built on Gemini 3 Pro and can incorporate real-time information via Search grounding, which returned more accurate renderings of specific named subjects in our set than GPT Image 2's parametric knowledge alone. Search grounding is separately metered at $14 per 1,000 queries after a free monthly allowance, so this round is a capability win with a cost caveat attached. How we measured it: A 20-prompt set requiring accurate depictions of specific named landmarks, lesser-known objects, recent events, and factual details, scored against ground-truth references. For Nano Banana Pro, Search grounding was enabled where available. |
OpenAI’s image API now offers four GPT Image models: GPT Image 2 (current flagship), GPT Image 1.5 (previous flagship), GPT Image 1 (deprecating October 23, 2026), and GPT Image 1 Mini (cheapest). Google’s flagship lane is Nano Banana Pro, Google’s most advanced image-generation and editing model, built on Gemini 3 Pro, priced at $2 per million input tokens and $12 per million output tokens. With DALL·E fully retired from the OpenAI API and Imagen 4 positioned as a cheaper, less-capable lane inside Google’s stack, this is now the flagship-vs-flagship comparison for teams choosing a single high-end image API.
Reading the result
The overall margin is two points, and the round tally is close: GPT Image 2 wins three rounds (prompt adherence, entry cost, premium-tier cost), Nano Banana Pro wins four (text rendering, multi-subject identity, resolution, grounding). The tally would give Nano Banana Pro the edge if every round were weighted equally, but the two OpenAI wins on cost are structural. They hold across almost any usage pattern, while three of Nano Banana Pro’s wins are conditional on the specific asset a buyer is producing.
How the rounds map to a buying decision
If the image needs to contain a lot of readable copy, a poster, an infographic, a UI mock, a product diagram, Nano Banana Pro is the more defensible pick. Nano Banana Pro is the best model for creating images with correctly rendered and legible text directly in the image, whether you’re looking for a short tagline or a long paragraph. Gemini 3 is strong at understanding depth and nuance in image editing and generation, especially with text. You can now create more detailed text in mockups or posters with a wider variety of textures, fonts, and calligraphy. With Gemini’s enhanced multilingual reasoning, you can generate text in multiple languages, or localize and translate content to scale internationally and share content more easily with friends and family.
If the deliverable needs to be 4K or a non-standard aspect ratio, GPT Image 2 forces an upscaling step. It generates images in square (1024×1024), portrait (1024×1536), or landscape (1536×1024). On GPT Image 2, the widescreen and portrait ratios are slightly cheaper at Medium and High tiers ($0.041/$0.165 vs $0.053/$0.211 for square). Nano Banana Pro removes that step: it adds fine-grained creative controls such as localized edits, lighting and focus adjustments, camera transformations, and support for 2K/4K outputs and flexible aspect ratios.
If the image contains several recognizable people, a group portrait, a storyboard cast, a campaign carrying a set of characters, Nano Banana Pro’s five-subject preservation is the more relevant signal. Nano Banana 2 can maintain character consistency for up to five characters and fidelity of up to 14 objects in one workflow for storytelling. Nano Banana Pro inherits and extends that behavior at higher fidelity.
If the workload is high-volume drafting, prompt iteration, or throwaway variants, cost tilts the decision back to OpenAI. Use GPT Image 1 Mini Low ($0.005-$0.006) for high-volume, cost-sensitive projects and prompt iteration. Use GPT Image 2 Medium ($0.041-$0.053) for general production work. Use GPT Image 2 High ($0.165-$0.211) for client-facing marketing assets and hero shots. Nano Banana Pro doesn’t have an equivalent Low tier: at $0.134 per standard image and $0.24 per 4K image, a project generating even a few hundred images per day can face serious budget pressure.
On the underlying model bets
OpenAI and Google have taken visibly different approaches to what a flagship image model is for. GPT Image 2 is built as a general-purpose creative generator with a wide quality-vs-cost surface: three quality tiers, three fixed aspect ratios, and a per-image list price that starts at half a cent. OpenAI’s GPT Image 1 generates and edits images via the dedicated Images API, and features accurate text rendering, transparent backgrounds, and up to 16 reference images for edits. GPT Image 2 extends that lineage on quality without changing the shape of the product.
Nano Banana Pro is built as a reasoning-heavy design tool. It’s Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with improved multimodal reasoning, real-world grounding, and high-fidelity visual synthesis. The model generates context-rich graphics, from infographics and diagrams to cinematic composites, and can incorporate real-time information via Search grounding. It offers strong text rendering in images (including long passages and multilingual layouts), consistent multi-image blending, and accurate identity preservation across up to five subjects. That reasoning layer costs tokens: Gemini 3 Pro Image uses reasoning capabilities to interpret complex prompts, and these thinking tokens are billed at the standard output rate of $12.00 per million tokens. For simple prompts, thinking overhead is negligible, but for complex multi-step generation instructions, it can add 5-15% to your effective per-image cost.
On the cheaper Google lane
Buyers who don’t need Nano Banana Pro’s specific strengths have a Google option that changes the cost calculus. Google DeepMind is launching Nano Banana 2, a new image model that combines the advanced features of Nano Banana Pro with the speed of Gemini Flash. You can now access high-quality image generation with faster editing and iteration across Google products like the Gemini app and Google Search. On that Flash lane, if you can accept 1K resolution and faster but less detailed output, the Gemini 2.5 Flash Image model offers dramatically lower pricing at $0.039 per image standard, or $0.0195 per image through the batch API. That’s the closer analogue to GPT Image 2 Medium on price, but it doesn’t carry Nano Banana Pro’s 4K, five-subject identity, or long-passage text-rendering strengths, so it doesn’t change the flagship-vs-flagship read, only the picture of Google’s overall stack.
On production plumbing
Both APIs are OpenAI-compatible drop-ins in most SDKs, so integration cost is a wash. The meaningful production difference is the shape of the request. Total request cost = text input + image input (if editing or using reference images) + image output + some billed text output behavior on certain requests. If you’re doing plain generation from a short prompt, the output-image price often dominates and the simple per-image estimate is usually close enough for planning. But the moment you start doing edit workflows, multi-image references, or higher-fidelity input preservation, the request stops looking like a pure pay-per-output-image product. The same caveat applies to Nano Banana Pro, where thinking tokens can add to the invoice on complex prompts. Teams pricing either model should measure accepted images against total billed attempts on their own workload rather than trusting a single per-image number.
- https://platform.openai.com/docs/models/gpt-image-1
- https://developers.openai.com/api/docs/pricing
- https://deepmind.google/models/gemini-image/
- https://blog.google/innovation-and-ai/products/nano-banana-pro/
- https://ai.google.dev/gemini-api/docs/image-generation
- https://openrouter.ai/google/gemini-3-pro-image
Hana Koizumi evaluates image, audio, and agentic tool use. She writes the task suites that probe vision and function-calling reliability, and she scores how a product behaves when it has to act, not just answer.