GPT Image 1.5 vs Nano Banana Pro — How to Use OpenAI’s Latest Update (Full Guide)

Two flagship image models dropped days apart. GPT Image 1. 5 and Nano Banana Pro. Open AAI claims their new model is up to 4x faster. Google pushes visual context. Let Nano Banana Pro handle up to 14 reference images at once. Marketing is one thing. Real usage is another. Same prompt, same tests. In the next few minutes, you'll know which one actually fits your workflow, which one is not worth wasting your time on. We're testing five categories: text rendering quality, editing precision, multi-element composition, speed, and factual accuracy. No fluff, just identical prompts, and sidebyside output, so you can see the differences for yourself.

Text rendering is one of the hardest challenges for AI image models. Small text, dense layouts, and special characters still break easily. GPT image 1. 5 says it handles smaller, denser text better than Delhi. Nano Banana Pro emphasizes multilingual text, translating and rendering multiple languages inside one image. Let's start

with generating a receipt. Create a realistic thermal receipt on a plain white background like a grocery store receipt. Keep everything black and white, narrow receipt shape with small, dense text that stays readable. Here's GPT image 1. 5's output. The receipt looks clean and consistent. The header is sharp, the item lines stay aligned, and the prices are easy to read. Now, zoom in at the bottom. The barcode digits are still readable, and even that tiny footer line, the authentication and terminal info stays surprisingly clear instead of turning into mush. Now, Nano Banana Pro's output. At first glance, it looks similar, but when you open it full screen, the difference becomes clearer. The image comes through at a higher resolution with more overall sharpness across the receipt. The structure holds up well, the text stays readable, and the fine details, especially around the barcode and footer, look cleaner at large scale. Both models handle small, dense text well. The only difference is that Nano Banana Pro outputs a higher resolution image. For the next test, I'm

switching to the AI Master platform, which I actually developed myself. This doesn't change the prompt or the models in any way. It just gives me more control over the workflow. You'll see later why this setup can even be better. Second test. Create an event poster for an international food festival. Include the title global food festival in English, festival global in Spanish. Add the date March 1517, 2026, and location park, New York. This is where Nano Banana Pro should shine. Multilingual text is one of its headline features. But let's start again with GPT image 1. 5. And the poster follows the prompt cleanly. English and Spanish are sharp and easy to read. The Arabic line visually matches what was in the prompt. The characters connect properly and the direction looks right, but without speaking Arabic, I can't say that with absolute certainty. The date and location are correct and stay crisp. So, overall, this works as a solid multilingual poster. Now, let's switch the model to Nano Banana Pro and run the exact same prompt. All three languages are present and readable. The date and location are correct and clearly placed. Overall, the prompt is followed properly, just interpreted through a very different aesthetic lens. Both models handle multilingual text well and follow the prompt without obvious errors. GPT image 1. 5 leans toward a more dramatic poster style look, while Nano Banana Pro goes for a cleaner, illustrated approach in terms of correctness and readability. This one is essentially a draw. The difference comes down to style preference rather than

capability. Most of generation this video is happening inside a master studio. Both GPT image and Nano Banana Pro are already there. Plus, we've got Sora 2 Pro, VO3. 1, Clank 2. 6, AI, Avatar, Motion Control, and a bunch of other models integrated. The main reason I like it, everything's in one place. Generation tools, courses, prompts, all in the same tab. Here's what actually makes it useful. You get the full generative AI essentials course. More than 190 lessons on prompting workflows, how these models actually work, plus the AI master method if you want to build something real with AI or turn into service. And the prompt lab has over 300 ready prompts so you're not starting from zero every time. You can share your generations, browse what others are making, and if someone downloads your work, you get a portion of tokens back, which you can later cash out. On the right here, you can see the chat window. It's trained specifically on all our lessons and platform content. So, when I ask it to how to improve a prompt or which model to use for a specific task, it's not giving you generic chat GPT responses. It actually knows the material inside the platform and everything you generate. No watermarks. Sora, VO, Nano Banana. Clean outputs unlike the official interfaces that burn branding into your files right now. 30% off annual pro. That's courses, tools, prompt lab, AI mentor, 2,000 tokens monthly that roll over. Link in the description below. Well, it's hard to

argue that both models handle text extremely well, but what about editing precision? You often start with a base image and need to adjust specific elements, change a background, swap clothing, modify colors without breaking the rest of the composition. GPT image 1. 5 claims it can change only what you ask while keeping lighting, composition, and people's appearance consistent. This enables believable clothing tryons, hairstyle changes, and background swaps. Nano Banana Pro emphasizes its expanded context window. You can upload multiple reference images to guide edits and maintain brand consistency. Let's test both approaches. I'm uploading this

portrait photo, a man standing outdoors, neutral expression, natural daylight. The prompt is replace the man's sweater with a formal navy blue blazer over a white shirt. Keep the face, hairstyle, body shape, pose, background, and lighting identical. GPT image 1. 5 handled the clothing change successfully. The outfit was replaced cleanly and the overall composition was preserved. There are minor changes in facial details, but they're subtle. Overall, the result looks solid. So, what does Nano Banana Pro show here? The model slightly zooms the subject out and introduces small facial distortions. The face changes subtly and the neck appears smaller. In this test, GPT image 1. 5 preserves the subject more accurately.

Now, let's switch to a more practical multi-reference edit. I'm uploading three reference images. One photo of myself standing in front of a mirror and two separate photos of plush toys. The task is simple. Combine all three into a single realistic image. The prompt, add both plush toys into my hands. Keep exactly two arms and two hands. Keep my face and lighting unchanged. Google highlights that Nano Banana Pro supports reference images, but you can do the same with GPT. OpenAI provides this through the API in edit image mode. GPT image can take up to 16 reference images on the AI master platform. You just click edit image to access this workflow which isn't available in the standard chat GPT image interface. So let's see how GPT handles this setup. It does a solid job overall. Both toys are added cleanly. The pose looks natural and the image holds together well. If you don't know the real size of these toys, the result feels convincing. Now, let's look at Nano Banana Pro. Here, the model clearly understands the scale better. The toys feel closer to their real size and weight. But there's a trade-off. The face takes a hit. The eyes look slightly blurred, and some facial detail is lost, which immediately breaks realism once you notice it. GPT image 1. 5 preserves the person and facial details better, while Nano Banana Pro handles object scale more accurately. Which one works better depends on what matters more in your edit, the subject's identity or physical realism of added objects.

Adding elements is one thing, but removing an object while keeping everything else intact is usually much harder for image models. The prompt, remove the plush dice toy from the image. Keep the person, pose, lighting, background, and all other details unchanged. GBT handles the object removal really well. The plush cube is gone and the rest of the image stays almost completely intact. The only noticeable change is a slight shift in brightness around the face area. It gets a bit darker, but the original photo wasn't shot in great lighting. So, this feels more like a lighting side effect than an actual mistake. Overall, GPT Image 1. 5 does exactly what was asked, cleanly and confidently. Let's check out the Nano Banana Pro. It also removes the object successfully, keeping the pose, background, and framing intact. Just like in previous tests though, there's a subtle change to facial details. In this case, the eyes look slightly different. Otherwise, everything looks great. Both models pass the test and remove the object without breaking the image. GPT image 1. 5 stays closer to the original photo, while Nano Banana Pro still leans towards small facial adjustments even in a simple removal task. So far, we've been working with

fairly controlled edits. A single person, a few added objects, and clear constraints. Now, let's move on to a different type of challenge. Multi-element composition tests. How well a model handles more complex scenes, multiple objects, multiple subjects, and correct spatial relationships between them. Here, we'll focus on two scenarios. A crowded marketplace with specific elements that need to be placed correctly and a multiobject product setup where arrangement and balance matter. Create a

bustling outdoor marketplace. Include a fruit vendor on the left with apples, oranges, and bananas displayed. A textile vendor in the center with colorful fabrics hanging. A customer on the right examining pottery background with market tents and string lights. Warm afternoon lighting. GPT image 1. 5 puts the scene together cleanly. On the left, there's a fruit vendor with apples, oranges, and bananas neatly stacked, even packing fruit for a customer. In the center, a textile stall is covered with colorful fabrics hanging vertically. And on the right, a woman is carefully examined in a clay pot. The background stays busy but readable with market tents, people moving through the scene, and string lights overhead. So, what does Nano Banana Pro show in the same scenario? The scene feels more raw and crowded. The market looks genuinely busy with tighter spacing and more people packed into the frame. It sells the idea of an active marketplace really well. The trade-off shows up in the distance. Faces in the background lose clear proportions and start to blur together, turning into less distinct shapes. GPT image 1. 5 keeps the scene cleaner and easier to read, while Nano Banana Pro makes it feel more crowded and alive, but loses detail in the

background. All right. In the marketplace scene, both models could hide behind crowd energy. If distant faces get messy, you can almost get away with it. But in a team photo, there's nowhere to hide. The headcount has to be exact. The left to right order has to match, and the poses have to be right. Prompt: Create a professional team photo with five people from left to right. Person in blue shirt with arms crossed. Person in red dress with hands clasped. Person in center and gray suit with hands in pockets. Person in green shirt pointing forward. person on far right in black outfit waving. All five should be standing facing camera with a modern office background. First, I'm running this prompt through GPT image 1. 5. It follows the brief well. Five people all standing facing the camera and the key gestures are there. The main thing that breaks the realism is the casting. The guy in the gray suit and green shirt are clearly brothers in the AI universe. Now I'm taking the exact same prompt and running it through Nano Banana Pro. The result reads more like a real office photo. Faces look more natural, lightening and depth feel more believable, and the frame has fewer generated vibes. It still matches the lineup and gestures, but the realism is stronger. Both models follow the prompt and get the structure right, but Nano Banana Pro delivers a more realistic looking result. After

busy scenes with people, let's move to a clean product showcase and test pure composition and placement. Create a product showcase for a skin care brand. Arrange the following on a marble surface. A serum bottle on the far left, a cream jar in the center, elevated on a small pedestal, a face mask tube on the right, fresh eucalyptus leaves in the background, soft natural lighting from the left creating gentle shadows. GPT image 1. 5 result. The composition clearly follows the prompt. The serum is on the left. The cream is centered on a marble pedestal. And the face mask sits on the right. The eucalyptus leaves fill the background naturally, and the light from the left creates soft, realistic shadows. Overall, it looks like a clean, well-composed product shot suitable for e-commerce or branding. When you zoom in, the smallest text on the serum label is slightly distorted and not perfectly readable. It's subtle, but noticeable on close inspection. Nano Banana Pro shows us a very polished, premium looking setup right away. The lighting, materials, and overall mood feels strong and visually appealing, very close to real skinincare brand shoot. However, under the same conditions, text handling is weaker. There are repeated words like serum, and cream. And some label lines read more like placeholder text than finished copy. It's very possible that with strict predefined text for each product, Nano Banana Pro could handle this much better. But since both models were tested under identical prompts, this first result shows GPT Image 1. 5 making fewer textual mistakes. Overall

speed also matters. When you're iterating on designs, running AB tests, or generating multiple assets, generation time directly affects productivity. OpenAI states that GPT image 1. 5 can be up to four times faster than DAL E and in practice you can generate images back to back without waiting on each result. So instead of relying on claims let's measure how both models perform side by

side. Prompt create a modern living room with a gray sectional sofa, glass coffee table, large abstract painting on the wall and floor toseeiling windows with city view. We enter the prompt for GPT and start the timer as soon as generation begins. GPT image finishes in about 25 seconds, which is a solid result for an interior scene. The output matches the prompt. A gray sectional sofa, glass coffee table, abstract wall art, and floor toseeiling windows with a city view are all present. If we look closer, a few imperfections appear. The plant on the left has slightly inconsistent leaf shapes, and the city skyline outside the window shows mild distortions. Some buildings look warped and lose clean architectural lines. Overall, the image works well at a glance, but these small issues show up when you start inspecting details. Same time test for Banana Pro. We start the generation and run the timer. The image completes in about 24 seconds, which is slightly faster than GPT, though the difference is small. The composition comes together cleanly. All prompt elements are present. Interior proportions feel stable, and the city view outside the windows looks consistent without obvious distortions. The abstract painting, sofa, and glass table are well balanced, making the result visually cohesive and harder to nitpick than the GPT output. Quick takeaway: Banana Pro is marginally faster here, but the speed gap is minor. The more noticeable difference is in overall visual consistency. Now, let's

move to a parallel workflow test. GPT image 1. 5 supports parallel generation. IQ five different prompts. A living room, an office, a kitchen, a bedroom, and an exterior shot. I can start all five at once and keep working while they generate. In Nano Banana Pro, in its native interface, the behavior is different. Prompts are processed sequentially. You submit one, wait for it to finish, then move on to the next. In a direct UI to UUI comparison, GPT clearly has the advantage here thanks to built-in parallel generation. This is where working with image models through an API changes the picture. When I switch to AI master and select Nano Banana Pro, I can submit the same set of prompts one after another and they generate simultaneously. The same parallel workflow just exposed in a different way. So in the original interfaces, GPT would clearly win on speed and convenience since parallel generation works out of the box. With an API based setup, both models end up offering very similar workflows. The real difference comes down to how each tool makes these features available. Search grounding is

Nano Banana Pro's unique feature. The model can research topics via Google search before generating images, helping produce more factually accurate diagrams, maps, and infographics. This is huge for educational content, data visualization, and instructional graphics where accuracy matters. GPT image 1. 5 doesn't have this feature. It relies on training data, which can be outdated or incomplete. Let's test whether search grounding actually improves accuracy. prompt. Create an

infographic showing the five steps to brew the perfect chai tea. Include ingredient proportions and steeping times. GPT generates an infographic that follows the prompt structure. Five steps, ingredients, and timing are present. The recipe is broadly correct for masala chai, but some details are generalized, proportions are simplified, and the process reflects a common average version derived from training data rather than a specific sourced recipe. But what does Banana do in the same scenario? Nano Banana Pro uses search, studies the topic, and then creates the infographic. The steps, proportions, and timings closely match commonly published masala chai recipes. Ingredient order, simmering times, and milk integration follow standard cooking guides rather than a generic outline. The infographic feels more grounded in an external reference rather than an inferred recipe. So both images follow the prompt well, but Banana's result appears more researchdriven, while GBT relies on generalized training knowledge. Now let's move to a more

demand and accuracy test. Geographic data. Maps are a good stress test for factual grounding locations, rankings, and numbers all have to be correct at the same time. Prompt: Create a map showing the five largest cities in Japan by population. Label each city and include population figures. GBT generated a map in a cool vintage style. It looks great at first glance, but let's check the details. First off, it gave me six cities instead of the five I actually asked for. And if you look closely at the map itself, I can see that several cities are clearly not in the right places. It looks nice, but the accuracy just isn't there. I won't bother comparing the exact population of every city. Let's just judge them based on the overall results. Will banana surprise us? This is a completely different modern look. I immediately see exactly five cities just like the prompt said. Most importantly, the cities are actually where they're supposed to be this time. It even labeled the major islands, which is a really nice touch that makes the map much easier to read. So, who handled this better? Both models actually got the population numbers very similar and mostly correct. The slight differences in millions happen simply because one model might count just the city center while the other counts the whole metropolitan area. But since Banana got the geography and the city count right, it's the clear winner here. So which one

should you choose? GPT Image 1. 5 is the control and fidelity pick. In our tests, it was more reliable when you need targeted edits without the subject drifting, especially on identity sensitive work like clothing swaps or multi-reference edits where the face has to stay intact. It also made fewer mistakes on product style text under the same prompt conditions. Nano Banana Pro is the polish and grounding pick. It often looks more premium and more alive in complex scenes. And it can be better at physical realism like object scale, even if facial detail sometimes takes a hit. But the real differentiator is search grounding when the image has to be fact-based maps, instructional infographics, diagrams. Banana has a clear edge because it can use Google search before generating. Both models are production ready. There's no universal winner. It depends on your workflow. This space moves fast.

What's true today could shift in three months. That's why I'll keep testing these models as they update. And I will post the results so you don't have to guess what still holds up. So, don't forget to hit subscribe. And if you want to test both models yourself without watermarks, unlike the official interfaces, get access to the full courses I mentioned, and even earn from your own generations, check out AMSer Pro. Link below. 30% off annual is still live. Which model are you leaning towards right now? GPT Image or Nano Banana? Tell me in the comments. Thanks for watching. I'll see you in the next one.