In-Depth Comparison: GPT-Image-1.5 vs Nano Banana Pro — Generation Quality, Editing Power, Pricing, and How to Choose (Explained Simply)
- If you want stable “only-the-change-I-asked-for” photo edits: GPT-Image-1.5 (prioritizes preserving composition, lighting, and a person’s appearance—i.e., the “must-not-change” elements)
- If you want to build infographics or text-heavy designs while “researching as you go”: Nano Banana Pro (supports real-time visualization using Google Search grounding)
- The fastest way to decide is by resolution, reference images, and workflow fit: Nano Banana Pro supports 1K/2K/4K and up to 14 reference images; GPT-Image-1.5 stands out for the ChatGPT Images experience and its API pricing structure
Bottom line first: Four “axes” to focus on in this comparison
Both GPT-Image-1.5 and Nano Banana Pro provide value not only in “generating images,” but in editing existing images exactly as intended. Still, their strengths lean in slightly different directions. If you choose using the following four axes, you’ll rarely get stuck.
- Editing stability: For example, you only want to change someone’s outfit, but the face or lighting also changes—how well can the model prevent those “accidents”?
- Text rendering: Whether text in posters, menus, or diagrams is actually readable is critical in real work.
- Resolution, reference images, and consistency: For production assets, 2K/4K output and consistency across multiple references matter a lot.
- Pricing and operations: Whether you’re doing manual, small-batch work or running high-volume generation via an API can completely change the “real cost.”
From here, I’ll organize things gently, based on officially stated specs (delivery model, pricing, features).
What are these, really? Who are GPT-Image-1.5 and Nano Banana Pro?
GPT-Image-1.5 (OpenAI)
GPT-Image-1.5 is presented by OpenAI as a flagship image generation model that powers the ChatGPT Images experience. Officially highlighted characteristics include strong performance at editing uploaded images while “preserving what must be preserved, and changing only what was requested,” plus up to 4× speed improvements. It’s described as strong not only for edits but also for compositing and transformations, with improved text rendering as well. On ChatGPT it’s packaged as a new Images experience, and on the API side it’s available as GPT Image 1.5.
Nano Banana Pro (Google / Gemini)
Nano Banana Pro is presented by Google within Gemini’s image generation/photo editing framework as a high-accuracy “Pro” variant, corresponding to Gemini 3 Pro Image (gemini-3-pro-image-preview). In Gemini’s descriptions, compared to Nano Banana (the fast mode), Nano Banana Pro runs in a “thinking mode” and emphasizes more precise control, advanced text rendering, leveraging world knowledge, and improved photo compositing. Developer docs also explicitly mention Google Search grounding and the ability to blend up to 14 reference images into a final output—features that read as very production-pipeline-oriented.
In one sentence: GPT-Image-1.5 tends to be about “faithful editing and a smooth creation experience,” while Nano Banana Pro tends to be about “pipeline strength: search grounding, many references, and production control.”
Comparison (1): Editing strength — how well can it do “change only this” in photos?
GPT-Image-1.5: Preserve what must remain, and change only what should change
OpenAI describes GPT-Image-1.5’s editing as preserving lighting, composition, and a person’s appearance while reflecting intended changes down to small details. This shines in practical cases like:
- Swapping only the background in an e-commerce product photo (keeping product color and shadows consistent)
- Creating hairstyle/clothing try-on images (not changing the face)
- Replacing only assets while keeping an existing banner layout intact
In these scenarios, “the model doesn’t spontaneously alter the overall vibe” is what determines usable quality. GPT-Image-1.5 is positioned strongly around that.
Nano Banana Pro: More control in details and more complex production instructions
Gemini’s descriptions say Nano Banana Pro offers more accurate control over editing variables like lighting, camera angle, and aspect ratio. Developer docs also emphasize strength in complex workflows (multi-turn creation and revisions). This makes it suitable for production where requirements easily become long—e.g., “product photo + diagram + caution notes + multilingual text” and other “spec-heavy designs.”
It’s not simply “which is better,” but which direction your edits lean:
- If you prioritize stable “minimal edits without breaking the photo,” choose GPT-Image-1.5
- If you want “build up a complex deliverable with reasoning and many constraints,” choose Nano Banana Pro
That’s a practical starting point for decision-making.
Comparison (2): Text-in-image and design use cases (text rendering)
Text in images is a classic weak point for image generation. Unreadable text is unusable in real projects.
GPT-Image-1.5: Improved even for dense, small text
OpenAI says GPT-Image-1.5 improves text rendering further, handling denser and smaller text better. Demos include recreating newspaper-like layouts with lots of text as images. So it’s not only “a poster headline,” but also information-dense, paper-like or document-like visuals.
Nano Banana Pro: Built for “practical text” like infographics, charts, menus
Google describes Nano Banana Pro (Gemini 3 Pro Image) as suited for infographics and data visualization, and as “ideal” for rendering readable text from short to long form in text-containing images. It also provides examples of visualizing real-time information like weather or sports using Google Search grounding.
So it’s not just text quality—Nano Banana Pro shows a philosophy oriented toward information design: internal diagrams, event flyers, store POPs, study summaries—visuals meant to explain.
Comparison (3): Resolution, reference images, and consistency (usable as production assets?)
Nano Banana Pro: 1K/2K/4K + up to 14 reference images are explicitly documented
Gemini 3 Pro Image (Nano Banana Pro) is documented to support 1K/2K/4K output. It can also blend up to 14 reference images into a final output, with concrete guidance such as “up to 6 object images for high-fidelity incorporation” and “up to 5 person images to maintain identity consistency.”
This level of “explicit counts and roles” is powerful when designing a production workflow. For example:
- Use multiple photos of the same person to generate consistent alternate shots or composites
- Reference multiple product angles to produce a unified key visual
- Combine character sheets, clothing references, and props to maintain style consistency
GPT-Image-1.5: Emphasis on pricing structure and a “don’t break the edit” philosophy
GPT-Image-1.5 also supports image inputs and outputs, and in ChatGPT, uploaded-image editing is a core experience. The messaging repeatedly emphasizes “preserving key elements,” so it’s easier to think of it as strong for workflows where you carefully refine one (or a small number of) base image(s) rather than building from many references at once.
Both can be used for asset production, but practically:
- Want consistency using many references → Nano Banana Pro
- Want reliable edits that preserve a base image’s quality → GPT-Image-1.5
This split maps well to real-world needs.
Comparison (4): Pricing and operations (personal use, team use, high-volume API)
Because “best” depends on volume and workflow, here’s a readable summary of what’s stated in official pricing materials.
GPT-Image-1.5 (OpenAI API): token-based pricing (text and image tokens separately)
OpenAI’s model pricing shows GPT-Image-1.5 API costs split into text tokens and image tokens (input, cached input, output). The specifics are shown as: text input $5/1M, cached input $1.25/1M, output $10/1M; image input $8/1M, cached input $2/1M, output $32/1M.
The key point: it’s not a fixed “per image” price. It varies with prompt length, image size, output conditions, etc. In prototyping, it’s safer to run a few iterations on the same theme and observe token growth before designing a high-volume pipeline.
Nano Banana Pro (Gemini API): image output is easy to estimate per image by resolution
Google’s Gemini API pricing page lists gemini-3-pro-image-preview (Nano Banana Pro Preview) image output in a very readable way. Under Standard, image output is $0.134 per image for 1K/2K, and $0.24 per image for 4K. It also lists token estimates: 1K/2K at 1120 tokens, 4K at 2000 tokens. Under Batch, prices are $0.067 for 1K/2K and $0.12 for 4K, roughly half.
For image inputs, it also provides an estimate like “about $0.0011 per image (560 tokens).”
Because “per-image cost by resolution” is explicit, Nano Banana Pro can be attractive for work where output volume is predictable (dozens or hundreds of assets for campaigns), making cost estimation straightforward.
Also: Check in-app caps and auto-switch behavior
Gemini’s guidance says that if you hit Nano Banana Pro’s limit, it automatically switches to Nano Banana (standard) (and if Nano Banana is also at its limit, usage becomes unavailable). If you’re testing in the app first, knowing this helps diagnose sudden output changes.
Which type of user fits which model? (Concrete personas)
This is usually the part people most want, so here it is in a practical way.
Who GPT-Image-1.5 tends to fit
- Social media / PR: Want quick turnaround editing of portraits/event photos without “breaking” them—e.g., “swap only background,” “remove only the unwanted object,” “keep the vibe but add seasonal elements.”
- E-commerce / small businesses: Product photo texture and lighting are critical, and you can’t afford edit accidents. Great for try-on visuals, color tweaks, etc.
- Designers (prototyping stage): Want to iterate quickly by dialogue—idea → edit → re-edit—inside ChatGPT’s Images experience.
- Developers who want to standardize on OpenAI: If you’re already using OpenAI text models and workflows, keeping images in the same stack can be simpler.
Who Nano Banana Pro tends to fit
- Information design / educational content: Creating diagrams, infographics, and explanatory visuals. Search grounding aligns with “research and summarize” workflows in visual form.
- Brand teams / agencies: Need consistency via many references and expect multi-turn revisions and scale production. The “up to 14 references” spec is directly useful for production management.
- Developers who want resolution-based cost estimation: Clear per-image pricing for 1K/2K/4K helps with budget planning for mass generation (ads, catalog-like outputs).
- Localization / multilingual teams: Google mentions multilingual text generation/localization possibilities, which can fit multi-language signage or explanatory materials.
Ready-to-use prompt examples (by use case)
To keep this practical, here are copy-pastable samples. The key for both is to clearly state what must not change.
Sample A: “Only this” photo edit (GPT-Image-1.5-style thinking)
- Goal: swap background + unify color tone
- Example:
- “Change the background to a white studio backdrop. Keep the person’s face, hairstyle, clothes, and lighting direction exactly the same. Preserve natural shadows.”
The trick is listing preserved elements first. The more complex the edit, the more “must-preserve” becomes your spec.
Sample B: Text-heavy poster (works for both; often stronger with Nano Banana Pro)
- Goal: event poster with large headline + small details
- Example:
- “A4 vertical poster. Top: ‘Winter Reading Fair’. Center: an illustration of books. Bottom: date, location, and fee in readable text. Plenty of whitespace, calm color palette.”
When text volume is high, specifying hierarchy (headline/body/notes) reduces breakage.
Sample C: Infographic (leans into Nano Banana Pro strengths)
- Goal: visualize a process
- Example:
- “Turn this into a 4-step infographic. Each step has a number, an icon, and a short description. Use two colors only. Prioritize readability.”
Since Nano Banana Pro is described with infographics in mind, this is a great first test.
Sample D: Consistency with reference images (using Nano Banana Pro’s spec)
- Goal: multiple shots of the same person with consistent styling
- Example:
- “Using the reference images of this person, create three shots (front, profile, 3/4). Same outfit and hairstyle. White background. Soft studio lighting.”
Because you have room for many references, you can also split “person + outfit + props” into separate inputs for stronger control.
If you’re stuck: a simple selection checklist (fastest route)
Answer these in order and you’ll naturally narrow it down:
- Do you absolutely need to preserve the original photo’s vibe?
Yes → prioritize GPT-Image-1.5 - Do you make lots of infographics/charts/explanatory visuals?
Yes → prioritize Nano Banana Pro - Do you need to estimate 4K costs and mass-produce outputs?
Yes → Nano Banana Pro (4K per-image pricing is explicit) - Do you want to use many reference images to maintain consistency?
Yes → Nano Banana Pro (up to 14 references explicitly stated) - Do you want to prototype and iterate via dialogue inside ChatGPT?
Yes → GPT-Image-1.5 (Images experience is provided)
It’s also realistic not to force a single choice—e.g., “infographics with Nano Banana Pro, photo edits with GPT-Image-1.5” is a straightforward split that often improves deliverable quality.
Wrap-up: They’re rivals, but their strengths are placed differently
Both GPT-Image-1.5 and Nano Banana Pro are clearly aimed at “image generation and editing you can use in real work.”
GPT-Image-1.5 centers on preserving key elements during edits and the hands-on creation feel through ChatGPT Images. Nano Banana Pro emphasizes pipeline-friendly specs like search grounding, many reference images, and resolution-based unit costs.
If you had to choose today:
- If you’re mainly editing photos or existing assets → GPT-Image-1.5
- If you’re mainly doing information design, scaling production, or leveraging many references → Nano Banana Pro
If you can, a great practical test is to run the same subject through both three times and compare (1) what kinds of “accidents” happen and (2) how easy it is to correct them. That tends to reveal the right fit for your environment very clearly.
References
- OpenAI: The new ChatGPT Images is here (overview of GPT Image 1.5)
- OpenAI API Docs: GPT Image 1.5 (specs & pricing)
- Google: Announcing Nano Banana Pro (Gemini 3 Pro Image)
- Gemini: Nano Banana Pro (in-app positioning & thinking mode guidance)
- Gemini API: Pricing (resolution-based unit prices for gemini-3-pro-image-preview)
- Gemini API: Image generation (up to 14 reference images and feature details)