AI Ad Battle: GPT-1.5 vs. NanoBanana Pro

AI’s early gold rush revolved around spectacle: viral portraits, surreal landscapes, and endlessly remixed memes. That era produced some stunning demos, but for marketers, a pretty image that can’t sell a product is just noise. The new arms race centers on something far more prosaic and profitable—ad-ready outputs that can plug straight into campaigns.

Marketers now care less about “generate me a dragon” and more about “take this iPhone photo of a serum bottle and turn it into five UGC variants for TikTok.” Real workflows look like this: you start with a product screenshot, existing brand shots, or a rough founder selfie, then iterate. Editing, versioning, and compliance-safe tweaks beat raw imagination almost every time.

That’s why simple text-to-image prompts feel dated. Teams need: - Consistent product geometry and branding - Fast A/B variants for hooks, backgrounds, and demographics - Predictable performance across dozens of SKUs

A one-off prompt can’t deliver that. A repeatable workflow can.

Enter what creators like Zubair Trabzada are quietly building: fully automated AI ad factories. Using no-code tools like n8n, a marketer can upload a Chanel perfume screenshot, feed in a short description, and spin up a pipeline that stores assets in Google Drive, auto-describes the image, generates optimized prompts, and outputs UGC-style creatives on demand.

These factories no longer require a dev team. A free n8n account, a JSON blueprint from a Skool classroom, and a VPS on Hostinger with an Additional Discount Code can stand up an AI ad line that runs 24/7. You’re not “using an AI model” anymore; you’re operating a small, software-defined studio.

That shift sets up the next fight. For this kind of automated ad assembly line, the question isn’t which model makes the wildest art—it’s which one quietly ships more profitable Ads. OpenAI’s new GPT-1.5 image model now claims the top spot on LM Arena for image editing, edging out long-time favorite NanoBanana Pro. When your revenue depends on UGC performance, that leaderboard suddenly matters.

“Most real marketing workflows aren't about creating images from scratch. They're about editing, refining, and iterating,” Zubair Trabzada says. That single line quietly kills the fantasy of brands spinning up viral campaigns from pure AI hallucination.

Pure generation breaks down the second a logo, bottle shape, or shade of blue actually matters. Models still routinely mutate product geometry, drift on color, and invent off-brand typography, which makes compliance teams and performance marketers equally nervous. For branded content, inconsistency is a bug, not a quirk.

Modern image editing flips the stack: start with a real asset, then surgically change everything around it. Trabzada’s workflow uploads a basic Chanel perfume screenshot, feeds it through an image describer, then asks GPT-1.5 or NanoBanana Pro to rebuild the scene as a luxury UGC ad. The bottle stays on-model; the world around it becomes malleable.

That means a single packshot can spawn dozens of variants tuned to different audiences and placements. One click can swap a plain background for a moody concrete wall, drop in a “handsome man in a tailored dark suit,” and adjust lighting to match luxury fragrance vibes. The hero product remains pixel-accurate, while the creative direction iterates at machine speed.

This is where GPT-1.5’s current lead on the LM Arena image-editing leaderboard matters. Trabzada calls out that GPT-1.5 now ranks above NanoBanana Pro, which he previously considered “by far the best model so far” for this job. That ranking turns subjective vibe-checks into a quantifiable metric: higher scores correlate with fewer artifacts, better object preservation, and more coherent compositions.

Marketers can now benchmark models the way they benchmark click-through rates or CPMs. If LM Arena says GPT-1.5 edits more reliably, you can justify routing thousands of automated UGC ad variants through it instead of NanoBanana Pro. Image editing stops being a gimmick and becomes an optimization problem you can actually measure.

Backed by OpenAI’s war chest and research pedigree, GPT-1.5 image arrives as the official challenger. Zubair Trabzada points to LM Arena, where GPT-1.5 currently sits at the top of the image editing leaderboard, edging out rivals in tasks like object preservation, style transfer, and contextual tweaks. On paper, it looks like the model you’d pick if you only trusted benchmarks and brand names.

NanoBanana Pro enters from the opposite direction: less hype, more receipts. Among AI UGC practitioners, Trabzada calls it “by far the best model so far” for this exact workflow, thanks to its ruthless product focus—keeping labels legible, bottles consistent, and compositions usable in paid campaigns. In his community, it has quietly become the default choice for automated product shots and UGC-style creatives.

This showdown does not try to crown a universal champion across art styles, anime, or photorealistic landscapes. The test narrows to a single, high-value lane: AI-powered User-Generated Content (UGC) ads that start from a real product image and end as ad-ready assets. If a feature does not move the needle on click-through rates or cost per acquisition, it does not matter here.

Stakes run higher than leaderboard bragging rights. GPT-1.5 leads LM Arena, but NanoBanana Pro dominates actual UGC pipelines where agencies crank out hundreds of variants per week using tools like n8n. The question is blunt: can the benchmark leader dethrone the practical favorite when both have to sell the same Chanel-inspired fragrance in a scroll-stopping feed?

For anyone tracking this arms race beyond YouTube tutorials, comparisons like GPT Image 1.5 vs Gemini Nano Banana Pro - Bind AI IDE show how quickly rankings can flip once workflows, hosting costs, and automation complexity enter the picture. This experiment pushes that tension directly into the UGC ad factory.

n8n sits in the middle of this whole experiment, acting as the connective tissue between half a dozen AI services that would otherwise never speak to each other. The no-code platform lets you drag, drop, and chain together APIs, turning what used to be a Frankenstack of scripts into a single, visual workflow. For marketers, that means GPT-1.5, NanoBanana Pro, cloud storage, and video tools all line up behind one “Run” button.

The ad factory starts with a simple form. A creator drops in two things: an image file (say, a Chanel perfume screenshot) and a short text description of the ad vibe they want, like “luxury seductive man’s fragrance UGC ad.” That manual input becomes the seed for an entire automated production line.

From there, n8n ships the raw asset to Google Drive for storage and versioning. The uploaded image gets a permanent URL, so every downstream step can reference the exact same product shot. No one has to dig through folders or Slack threads to find “final_final_v7.png.”

Next, an OpenAI-powered “image describer” node analyzes the product shot. It generates a structured description of everything in frame: bottle shape, label style, colors, lighting, background. That machine-written description then feeds into a dedicated prompt engineering step that fuses product details with the marketer’s creative brief.

At that point the workflow branches into two nearly identical paths. One calls GPT-1.5’s image editing endpoint; the other swaps a single URL to hit NanoBanana Pro. Both models receive the same base image and engineered prompt, then output polished ad-ready visuals that keep the original product but overhaul the context, model, and mood.

Those still images don’t just sit in a folder. A final stage pipes them into an AI video generator (for example, a VO-style model), which animates the UGC concept into a 10–60 second vertical ad. The result: a full funnel from screenshot to social-ready video with zero manual Photoshop or Premiere work.

For marketers, this pipeline changes the tempo of creative testing. One product image can spawn dozens of A/B variants across models, hooks, and scenes in a single afternoon, instead of waiting a week on a design team. The kicker: Zubair Trabzada publishes this entire professional-grade n8n blueprint as a free JSON file, and n8n itself runs on a free tier or a cheap self-hosted setup on Hostinger, so anyone can clone the same factory without paying agency software prices.

Forget hype videos and benchmark charts. Your own AI ad factory is about 15 minutes away, and most of that is copying a URL and uploading a file.

Start by grabbing the exact workflow Zubair Trabzada uses on his channel. Join the free AI Workshop community on Skool via this link, sign in with Google or email, then hit “Join” to unlock the classroom.

Inside Skool, jump to Classroom → YouTube Resources. Scroll to the bottom until you see the UGC ad blueprint, labeled as an n8n “blueprint” or template, and download the JSON file to your desktop.

Now you need somewhere to run it. Create a free n8n.cloud account, or Self-host n8n on a VPS via Hostinger at hostinger.com/aiworkshop and apply the Additional Discount Code AIWORKSHOP during checkout to cut hosting costs.

Once n8n is live, open the editor and create a new blank workflow. Click the three-dot menu next to “New Workflow,” choose “Import from File,” and upload the JSON you downloaded from Skool.

n8n will reconstruct the entire automation: HTTP form trigger, Google Drive upload, OpenAI image describer, prompt generator, and two nearly identical branches for GPT-1.5 and NanoBanana Pro. You get all the prompt logic and node wiring prebuilt, no manual config required beyond your API keys.

Model switching comes down to a single URL. Inside the HTTP Request node that calls your image model, you’ll see an endpoint like: - GPT-1.5: api.openai.com/v1/images/edits - NanoBanana Pro: api.nanobanana.ai/v1/images/edits

To A/B test, duplicate the branch, then change only that endpoint URL and the model name field (for example, `gpt-image-1.5` vs `nanobanana-pro`). Everything else in the workflow—prompt, seed image, output handling—stays identical.

From there, hit “Execute Workflow.” Upload a product screenshot, paste your ad description, and n8n will fire both models in parallel, returning side‑by‑side UGC creatives ready to plug into your next Ads test or “Learn How to Make Money with AI” funnel.

Chanel’s Bleu de Chanel becomes the crash-test dummy for this AI ad duel. Instead of a pristine studio asset, Zubair Trabzada grabs a basic Google Images screenshot of the signature blue bottle—logo, reflections, and all—and feeds that imperfect, very real-world input into his automation. The question: which model can turn this off-the-shelf product shot into a scroll-stopping UGC-style ad?

Both models receive the exact same text brief, copied and pasted with zero edits: “Create a luxury seductive man's fragrance UGC ad inspired by Bleu de Chanel featuring a handsome man in a tailored dark suit seated confidently against the wall.” No secret sauce, no hidden system prompt, just one sentence that any marketer could type into an interface. That constraint makes every difference in output traceable back to GPT-1.5 vs NanoBanana Pro, not prompt engineering wizardry.

Inside n8n, Trabzada hits “Execute workflow” on his prebuilt template and a minimalist form appears: one image upload field, one description box. The workflow first uploads the Chanel screenshot to Google Drive, then passes it through an OpenAI image describer node, which generates a structured understanding of the bottle, colors, composition, and brand-adjacent details. A second node turns that description into a refined image prompt tailored for each model’s API.

From there, n8n forks the automation. One branch calls GPT-1.5’s image endpoint, the other swaps in the NanoBanana Pro URL, but everything else—prompt, seed image, resolution, UGC styling—stays identical. Running them in parallel with synchronized inputs exposes subtle differences in lighting, skin texture, fabric detail, and how faithfully each model preserves the Bleu de Chanel layout.

For readers who want a deeper technical breakdown of these model behaviors beyond this ad workflow, GPT Image 1.5 vs Nano Banana Pro | AI Image Models Comparison offers a broader benchmark view. Here, though, the scoreboard is brutally simple: same screenshot, same sentence, same pipeline—two radically different UGC ads.

GPT-1.5’s output looks like a luxury cologne spread torn from a September issue. The bottle sits almost exactly where it appears in the original Google Images screenshot, but now it’s framed by a moody, desaturated environment with soft, directional light. Shadows fall cleanly, reflections behave, and the whole scene leans hard into a chic, editorial vibe rather than pure product shot.

The model in GPT-1.5’s render feels like part of that world, not a pasted-on asset. He wears a tailored dark suit, posed in a relaxed, confident lean against the wall, with his face partially obscured by shadow. Background elements blur just enough to keep the composition airy, while still conveying upscale, “French apartment at midnight” atmosphere that screams luxury.

NanoBanana Pro goes in a different direction: less GQ spread, more Sephora homepage. The bottle jumps forward in the frame with tack-sharp edges, high-contrast highlights, and a dark-to-light gradient that drags your eye straight to the logo. Where GPT-1.5 bathes the scene in subtlety, NanoBanana Pro cranks up dramatic lighting so the glass, cap, and label read instantly at thumbnail size.

The human subject in NanoBanana Pro’s render feels more like a supporting actor. He still wears a dark suit and still hits the “handsome, confident man” brief, but his pose and expression sit firmly behind the product in visual priority. Background detail recedes into deeper shadows, flattening into a stage that exists primarily to make the bottle pop for a high-intent, scroll-stopping UGC ad.

Prompt interpretation differences show up in the micro-decisions. GPT-1.5 takes “seated confidently against the wall” almost literally: the model interacts with architecture, uses body language, and shares the frame with the perfume as co-stars. NanoBanana Pro treats the same phrase as mood metadata, not blocking direction, and re-allocates visual real estate so the bottle dominates center frame.

For marketers, that split matters. GPT-1.5 excels at building a vibe—brand storytelling, aspirational lifestyle, and editorial polish that would slot into a lookbook or high-end carousel. NanoBanana Pro, by contrast, makes the product the undeniable hero, optimized for performance creatives where click-through depends on a single, crystal-clear focal point.

Shot as a vertical UGC clip, the GPT-1.5-based ad moves with the same quiet confidence as its still frame. Slow pans across the Bleu de Chanel bottle preserve the chic, moody lighting: cool blues, deep shadows, and a subtle falloff that keeps the label legible even as the camera drifts. Motion feels intentional rather than template-driven, more like a 15-second Instagram Story than a stock slideshow.

Transitions stay restrained. Gentle push-ins on the model’s face and suit fabric keep the focus on texture and posture, echoing the original “luxury seductive man’s fragrance” brief. No jarring zooms, no overcooked particle effects—just enough parallax and depth shifts to keep watch time high without screaming “AI-made.”

NanoBanana Pro’s video takes a more visibly produced route. Camera moves snap a bit faster, with bolder rack-focus moments that swing from the model’s jawline to the bottle’s glass edges, amplifying the premium feel. Highlights on the bottle catch and flare as if a practical light swung across frame, a small detail that sells the illusion of a real set.

Lighting effects in the NanoBanana Pro cut lean harder into drama. Micro-reflections crawl across the logo, and background gradients pulse just enough to suggest nightclub ambiance without drowning the product. For brands chasing a louder, high-gloss aesthetic, that extra motion reads as money on screen.

Seduction sits at the center of both spots, but they get there differently. GPT-1.5 plays the understated seducer: slower pacing, longer holds, and a near-perfect continuity between still and motion that feels like a single, coherent brand world. NanoBanana Pro seduces by spectacle—more kinetic energy, more light play, more “scroll-stopping” moments.

On raw output quality, neither model clearly wins. Both deliver ad-ready UGC videos from a basic Google Images screenshot and one prompt, with no manual keyframing or After Effects work. Choice here comes down to strategy: GPT-1.5 for restrained luxury and consistency across assets, NanoBanana Pro for bolder, flashier perfume Ads that want to shout a little louder in the feed.

No knockout punch landed here. GPT-1.5 and NanoBanana Pro walked out of the n8n ad factory looking less like rivals and more like two specialists who refuse to fight outside their weight classes. The “best” model tracks directly to what you’re trying to sell and how you’re trying to sell it.

For lifestyle brands and aspirational storytelling, GPT-1.5 has the edge. Its Bleu de Chanel shot wrapped the bottle in cinematic shadows, soft gradients, and that moody “Instagram editorial” glow that beauty and fashion teams pay photographers four figures for. If your funnel starts on TikTok, Instagram Reels, or YouTube Shorts with vibes first, details second, GPT-1.5 is the better match.

Think brands selling identity as much as product: grooming, skincare, boutique fashion, wellness, premium coffee, even SaaS tools trying to look like lifestyle products. You want GPT-1.5 when the goal is to: - Build aspirational mood and perceived luxury - Blend the product naturally into a human scene - Generate UGC-style creatives that feel “shot on a real set”

NanoBanana Pro pushes in the opposite direction: clarity over romance. Its outputs tend to favor tighter compositions, harder edges, and more legible packaging, which is exactly what high-intent shoppers on Amazon, Shopify, or Meta’s Advantage+ placements respond to. For direct-response marketers, that bias is not a bug; it is the feature.

Use NanoBanana Pro when you need: - High-contrast, scroll-stopping product focus - Clean e-commerce hero shots and carousel frames - Retargeting creatives where every pixel must scream “here’s what you’re buying”

Framed that way, the smart move is not to crown a universal champion but to treat these models like lenses in a camera bag. Marketers already mix wide, macro, and telephoto glass; now they can mix GPT-1.5 and NanoBanana Pro inside the same n8n automation, routing lifestyle top-of-funnel Ads to one and hard-sell, bottom-of-funnel variants to the other. The “winner” becomes your blended system, not a single model logo on your invoice.

Automation is only half the story; the real play is turning this into a productized AI ads service. If you can run a Chanel-style UGC pipeline once, you can sell that same underlying workflow 10, 50, or 100 times to brands that do not want to touch prompts, n8n, or model settings.

Most small e‑commerce brands already spend on creative, but their bottleneck is volume and iteration, not ideas. A repeatable n8n workflow that ingests a product screenshot, generates multiple edited images via GPT‑1.5 or NanoBanana Pro, and outputs a ready‑to-run UGC video becomes a packaged deliverable: “10 ad variants in 24 hours” instead of “we’ll see what the designer comes up with.”

That turns your setup into a clear value proposition: automated, on‑brand creatives at scale. You are not selling prompts; you are selling outcomes like lower CPCs, faster creative testing, and consistent product visuals across Meta, TikTok, and Amazon listings.

Agencies and freelancers can wrap this into simple offers: - One‑time “Ad System Setup” fee for building the n8n pipeline - Monthly “Creative Pack” subscriptions (e.g., 20 new UGC ads per month) - Higher‑tier retainers that include A/B testing and performance reports

Running this on a self‑hosted stack keeps margins high. Self‑host n8n on a VPS from Hostinger via hostinger.com/aiworkshop and stack it with the Additional Discount Code AIWORKSHOP to keep infrastructure costs low while you scale client volume.

If you want the business playbook, not just the JSON file, Zubair’s paid AI Workshop community covers how to Make Money with productized automations: positioning, pricing, client onboarding, and building a real automation agency instead of a side project. It extends the free Skool community with deeper systems for recurring revenue and operations.

You can Join the free AI automation community on Skool via this link, then upgrade into the “Learn How to Make Money with AI” program at this link once you have the workflow running. From there, the question stops being “Which model Is Better?” and becomes “Which client needs this next?”

What's the main difference between GPT-1.5 and NanoBanana Pro for ads?

The primary difference lies in their editing style and output aesthetic. GPT-1.5 often produces a chic, atmospheric result with balanced lighting, while NanoBanana Pro tends to create sharper focus and dramatic lighting on the product itself.

Why is image editing more important than generation for marketers?

Most marketing workflows involve refining existing brand assets like product photos, not creating images from scratch. Strong editing capabilities allow for rapid iteration, consistency, and adapting assets for different campaigns, which is more practical and efficient.

Is n8n necessary to use these AI image models?

No, you can access these models via their respective platforms. However, n8n is crucial for automating the end-to-end workflow of creating UGC ads, from image upload and prompt engineering to final video generation, saving significant time.

Can I really get the complete AI ad workflow for free?

Yes, the creator provides the full n8n workflow as a downloadable JSON file in their free Skool community, allowing you to replicate the entire process.