GPT Image 2 Wins on Layout While Nano Banana 2 Wins on Speed

GPT Image 2 and Nano Banana 2 are presented as two of the best AI image models available now. Both can generate and edit images, and both are available in ElevenCreative. The practical distinction is whether the job values prompt adherence, text hierarchy, and tight composition, or speed, detail retention, consistency across edits, and lower high-resolution cost.

GPT Image 2 is described as OpenAI’s newest image model, released in April. Its defining feature is that it reasons before it generates. Its claimed strengths are near-perfect text rendering and dense layouts in a single pass: magazine covers, pages, posters, and marketing assets where copy hierarchy matters. ElevenLabs illustrated that with a generated mock newspaper front page carrying a large headline, decks, market copy, and a dense front-page layout.

Nano Banana 2 is described as Google’s newest image model, built on Flash-class architecture and also reasoning before generation. Its headline advantages are speed and consistency: quick generations, coherent subjects and products across edits and generations, and favorable cost scaling as outputs move toward 4K.

That difference showed up repeatedly in ElevenLabs’ runs. GPT Image 2 tended to produce more deliberate, tighter, more editorially composed outputs, especially where a prompt specified framing or text hierarchy. Nano Banana 2 more often pulled back to show the whole subject, retained fine product details better in several cases, and handled some transformations more convincingly. It also appeared more prone, in these examples, to add unasked-for elements or spread attention across the whole scene.

At low and medium quality, the two models were described as “basically even” on generation cost. For one-off image generation, the difference is not expected to matter much. The cost distinction becomes important at high resolution: at 4K, Nano Banana 2 was said to land at roughly two-thirds the cost of GPT Image 2.

That changes the economics of scale. A single hero asset may not justify much concern over the difference. A batch of 50 product variations does. For that kind of workflow, Nano Banana 2 was described as the cheaper option “by a clear margin,” with the caveat that prices change quickly.

The on-screen cost chart supported the point visually: the low-quality 1K and medium-quality 2K bars for GPT Image 2 and Nano Banana 2 were comparable, while GPT Image 2’s 4K price bar was substantially longer than Nano Banana 2’s.

Speed produced an even clearer separation. At 2K resolution, Nano Banana 2 averaged about 20 seconds per image in the reported runs. GPT Image 2 at medium quality averaged about 55 seconds. That put Nano Banana 2 at roughly 2.4 to 2.8 times faster.

ElevenLabs did not pin the speed gap on a single cause. Generation times vary by time of day and may improve later. GPT Image 2 is newer and may have been under heavier demand. Nano Banana 2 also uses Flash-class architecture, which was described as optimized for fast generation. The observed gap was treated as probably caused by a mix of current demand and model architecture.

For single generations, the user probably will not feel the gap. For batch work or live iteration, where a user is constantly tweaking and regenerating, the faster render times become noticeable. The workflow advice was to iterate at lower resolutions first, then move up once a prompt is working.

ElevenLabs demonstrated a simple A/B setup in ElevenCreative’s Flows interface: create a text node, connect it to two image-generation nodes, set one to GPT Image 2 and the other to Nano Banana 2, then run both from the same input. The example shown used a simple “car” prompt connected to both nodes.

The point is not only convenience. The results show that the two models often comply with a prompt in different ways. GPT Image 2 was repeatedly characterized by its tendency to focus on a single subject and compose around it tightly. Nano Banana 2 was repeatedly characterized by its tendency to include more of the full object, full person, or full environment.

That pattern appeared in food, fashion, advertising, and image-combination examples. In a burger prompt, Nano Banana 2 showed more of the restaurant context, including the requested fries and drink in the background, while GPT Image 2 repeatedly chose a tighter shot of the burger itself. In a fitness apparel banner, GPT Image 2 cropped the runner more tightly and produced a result the presenter preferred; Nano Banana 2 showed the full body more consistently. In a combined-image edit, where a man was placed inside a house, GPT Image 2 made the man the focus, while Nano Banana 2 pulled back to make both the house and the man prominent.

Neither tendency is inherently better. If the job is a product hero shot, a close-up campaign image, or a layout where one focal point needs to dominate, GPT Image 2 may be more aligned. If the job needs the surrounding context preserved or a full object shown, Nano Banana 2 may be more naturally inclined in that direction.

GPT Image 2’s strongest results came from strict prompt following, composed framing, and designed text hierarchy. In the luxury serum bottle test, both models produced broadly similar product images, but Nano Banana 2 got the bottle cap wrong in both tested generations. GPT Image 2 got the cap right at low, medium, and high quality, including the requested plastic top section. The presenter also preferred GPT Image 2’s background lighting.

The visual comparison mattered because the flaw was concrete. The Nano Banana 2 images showed the same premium bottle form, but the cap details did not match what was requested. The GPT Image 2 comparison showed low-, medium-, and high-quality versions side by side, all preserving the cap structure more faithfully.

The same pattern appeared in the fashion editorial prompt. The prompt specified a woman in an oversized cream wool coat, a rain-slicked European street at dusk, a wide 16:9 frame, subject left of center, warm shop-window light, and an 85mm portrait-lens feel with shallow depth of field. GPT Image 2 placed and cropped the model closer to what the presenter had in mind. Nano Banana 2 placed her farther away and nearer the center.

Text-heavy work sharpened the distinction. In a social media ad banner for a summer fitness apparel brand, both models rendered the requested text — “RUN YOUR WORLD,” “New Summer Collection,” and “SHOP NOW ->” — and both adhered reasonably well. GPT Image 2 was preferred for its overall look and tighter model composition; Nano Banana 2’s text drop shadow was called out as less desirable.

The “BLOOM” magazine cover test made the layout difference especially visible. GPT Image 2 generated a cover with the masthead, issue date, and cover lines arranged in a cleaner hierarchy. Nano Banana 2 included more cover text, including “THE ART OF THE COTTAGE GARDEN,” “10 Perfect Perennials for Sun & Shade,” and “YOUR BEST SUMMER BORDER YET,” but placed text across more of the image in a way the presenter judged cheaper and less designed. GPT Image 2 was judged to have better composition, layout, and text hierarchy.

GPT Image 2 also handled some product-advertising and identity tasks better. In the smartwatch advertisement prompt, Nano Banana 2 repeated dashboard elements — including “78%” in multiple corners — and appeared to struggle with watches across multiple generations. GPT Image 2 looked “a little bit more slick” and hallucinated less in that case.

In the age-progression edit, GPT Image 2 produced 10-year-old, current, and 80-year-old versions of a man that were judged to look like the same person at different ages. Nano Banana 2’s result looked more like different actors playing the same character at different ages.

The throughline is control, not a universal advantage on identity. Nano Banana 2 won the character-reference-sheet identity test later in the comparison. But where the task depended on a designed layout, a precise focal point, or a source-faithful age transformation, GPT Image 2 was more often favored.

Nano Banana 2’s wins were not marginal. Several examples showed it producing more convincing detail or better preserving identity across transformations.

In the architectural exterior test, both outputs were described as recognizably AI at a glance, but Nano Banana 2 won. GPT Image 2 lost coherence in details, especially around the pool edge and steps. GPT Image 2’s colors were described as more aesthetic, while Nano Banana 2’s scene looked brighter and almost studio-lit despite being an outdoor house. Even so, Nano Banana 2 was judged stronger overall.

In the lifestyle e-commerce backpack prompt, both models produced strong images. The deciding detail was the zipper. Nano Banana 2 preserved the zipper better. GPT Image 2’s zipper became blurry and pixelated, with mismatched teeth that looked like they would not unzip.

A corporate team-photo prompt revealed a different kind of advantage. GPT Image 2 looked more realistic at first glance, but hallucinated more once multiple people appeared. One woman’s hand holding a cup looked strange, and in another GPT Image 2 generation a man in a green shirt appeared to have six fingers. Nano Banana 2 looked more polished and slightly more like AI, but produced fewer hallucinations in the examples shown. For team photos, Nano Banana 2 may be the better option if the user can accept the “stock AI feel,” because fewer hallucinations mean fewer regenerations and fewer wasted credits.

Nano Banana 2 also performed well in edits that required realism or consistency across views. From a single image of a woman in a coat, a character-reference-sheet prompt asked for an extreme close-up portrait and four full-body orthographic views on a white background, with consistent identity and clothing. Nano Banana 2 won on facial resemblance and fidelity to the original character. GPT Image 2 lost consistency across angles. Nano Banana 2 changed the coat color slightly, but it still looked like the same coat, with the collar folded upward.

In a face-enhancement and upscaling edit, GPT Image 2 stayed closer to the original portrait but remained somewhat plasticky. Nano Banana 2 went further in adding skin, hair, and facial detail, making the face look more realistic and human. In a cartoon-cat-to-photorealistic-image edit, GPT Image 2 stayed close to the original eyes and body shape but failed to make the result look photorealistic in the presenter’s judgment. Nano Banana 2 produced more convincing cat eyes and fur and won “by a long shot.”

The trade-off was explicit: Nano Banana 2 often gives more realism and richer detail, while GPT Image 2 may stay closer to the original structure or style. When the task specifically asks for realism, Nano Banana 2 can benefit from that willingness to reinterpret.

Neither model solved every task cleanly. The data-infographic example is the clearest warning. The prompt specified a clean annual-report graphic with a title, three exact stat callouts — “+127%,” “4.2M,” and “98%” — and a horizontal bar chart labeled Q1 through Q4 plus YTD in ascending order. Both outputs looked good and adhered to much of the prompt. But both hallucinated the bar-chart line lengths and percentages below the key stats.

The presenter’s conclusion was that even though both models are described as reasoning models, the reasoning step was “not quite there yet” for this use case. In his view, the models should have used the provided information as context to create the rest of the graphic accurately. For data infographics, the practical advice is to provide all the information, not half of it.

The image-combination example exposed another limitation: hidden context. The models were asked to place a man inside the living room of a house from an earlier image. Neither model actually knew what the interior of that house looked like. GPT Image 2 created a tight composition around the man and borrowed elements matching the house, including trees, a swimming pool, brick wall, and large windows. Nano Banana 2 also borrowed house-like elements but pulled back further to capture more of the environment. Under close inspection, the Nano Banana 2 result looked more hallucinated, with a confusing window or corridor area and furniture placement that did not resolve cleanly.

The photorealistic house transformation produced a more mixed result. Given a stylized painting of a yellow house with a red bicycle and asked to make it photorealistic, GPT Image 2 did better overall. At a glance, the GPT Image 2 image looked more realistic, though the leaves on the trees and ground were repetitive and brush-stroke-like. Nano Banana 2 took more creative liberties, and the composition and colors felt less cohesive — more like an artistic painting or heavily edited image than a straightforward photorealistic conversion.

These examples define the edge of the models’ apparent reasoning. They can produce plausible surfaces. They can often follow formatting and visual style. But when the task requires inferred numerical structure or unseen spatial knowledge, the user should not rely on either model to fill gaps accurately.

The editing examples make the distinction more specific. GPT Image 2 often preserved the original image’s placement, lighting, angle, and composition more closely. Nano Banana 2 often made the result cleaner, more editorial, or more realistic, but sometimes by changing the source more aggressively.

In a product-extraction example, the source image showed a product in a busy scene. The prompt asked the model to extract the core product, preserve original labels, text, and surface details exactly, and present it as a studio product shot on a white background at a slight three-quarter angle. GPT Image 2 extracted the product while keeping its exact shape, placement, color, and angle. Nano Banana 2 created a more editorial result and may have matched the prompt better on the requested three-quarter angle and color context. The practical distinction: GPT Image 2 may be better when fidelity to original placement and lighting matters; Nano Banana 2 may be better when the desired result is a cleaner product shot.

A second extraction used a transparent berry bowl in a cluttered environment. Both models performed well, especially given the transparent packaging and messy background. GPT Image 2 kept exact positioning, color, and lighting. Nano Banana 2 adapted the product to the blank white environment and angled it more from above. Again, GPT Image 2 gave fidelity to the original, while Nano Banana 2 gave a cleaner editorial look.

Resizing a horizontal running ad into a vertical 9:16 format favored GPT Image 2. The visible prompt text on screen appeared mismatched with the resize task, so the useful evidence is the resulting image. GPT Image 2 centered the shop sign at the bottom in a way the presenter compared to an Instagram Story call to action, and it placed text behind the runner’s arm. That layering detail made the output feel more designed. The win was described as mostly preference, but GPT Image 2 was favored.

Outfit replacement was closer. Both models did well when asked to keep the person’s face, skin tone, body shape, pose, lighting, and shadows while replacing all clothing and accessories with a coherent new outfit. GPT Image 2 hallucinated less and was more consistent in keeping the same composition, lighting, layout, and subject position while changing only the clothes. Nano Banana 2 sometimes produced better-looking outfit replacements, but occasionally generated a frame that diverged more from the original. The trade-off was stability versus occasionally stronger styling.

The final guidance is workload-specific. GPT Image 2 is the model to reach for when the prompt must be followed closely, when the desired image is a close-up product or model composition, or when marketing text hierarchy matters. Nano Banana 2 is the model to reach for when the job values detail retention, realism in some transformations, speed, and lower relative 4K cost. Nano Banana 2 may also be useful for multi-person shots, because GPT Image 2 hallucinated more in the team-photo examples.

The models’ weaknesses differ. GPT Image 2 can be slower, more expensive at 4K, and more prone in these examples to hallucinate details in multi-person images or lose fine object structure. Nano Banana 2 can take more creative liberty, add elements that were not requested, pull back when a tighter focal composition may be desired, and produce outputs that feel more visibly AI or less designed in layout-sensitive contexts.

A sharper operating rule follows from the examples:

• Use GPT Image 2 when the prompt is the contract: text hierarchy, exact composition, close product framing, source-faithful edits, and identity-preserving age variation.
• Use Nano Banana 2 when iteration cost matters: faster runs, lower relative 4K cost, fine detail, realism-heavy edits, and multi-person scenes where fewer anatomical hallucinations are more important than a less “AI” finish.
• Use both when the image matters enough that one model’s default interpretation could change the outcome.

Inside ElevenCreative’s Flow setup, that means routing one text prompt into both models and judging the outputs side by side before committing. The answer to “which one should you use?” is not a fixed ranking. Use GPT Image 2 when control and layout are the priority. Use Nano Banana 2 when speed, detail, and scalable iteration matter.