Transcription
Here's why Nano Banana is making a huge wave in the industry. Nanobanana can generate a 1024x1024 image incredibly fast with higher confidence than GBT image 1, Flux Context, Quen image edit, and even their previous Gemini 2.0 Flash image.
Nano Banana is priced at $30 per million tokens, which means it roughly costs about 4 cents per image generation. And given the speed and quality, it seems extremely affordable.
While there are no official documentations on how Google was able to achieve Nanobanana's capabilities, we can assume that Nano could refer to the size of the model being small through the process of post-training quantization, which is how Nano Banana is able to generate so fast while achieving higher quality.
For example, you can simply drop an image and ask the model to do something like make this into a textbook for computer science and the model will not only preserve the text inside the original document with blazing speed follow your prompt and essentially maintain their character consistency like this. Additionally, you can also add in a picture and make him into a movie star. And just as easily as that, you can also adjust the picture to make it rainy or even make it more dim to change the picture while not losing the character consistency at all.
So as you can see the implication of nanobanana are quite huge when it comes to industries that depend on Photoshop in providing image modification services like marketing, advertisement or even photo editing software. A typical time to edit photos can take up anywhere between 5 to 60 minutes. While this might vary depending on the skill level and the type of work that's involved, Nano Banana is yet another confirmation that we are closer in era of rendering manual work in photo editing completely outdated.
Let's first talk about speed. Netto Banana is able to generate at the speed of around 274 tokens per second. So, as you can imagine, the ability to iterate through creative work has now been raised where you can now build images on top of images in a pace that human beings have never been able to do before. It's really hard to accentuate how groundbreaking technologies like Nano Banana means to human advancement.
For basic photo editing tasks, you have brightness, sharpness adjustments, color corrections, cropping, contrast, exposure, transformation. These things that would typically take up to 5 to 10 minutes depending on the skill level. Net Banana can modify them in a matter of seconds. And for more detailed work, you have more creative tasks like removing layers, blending and brushing, polishing, texture, replacements, and inpainting where all these tasks will typically require more than 30 minutes are now able to be done in a matter of seconds just from a one prompt.
And beyond sheer speed, the quality of the output is shocking everyone in how it's able to maintain proper lighting and character consistency. Nanobanana marks Google's breakthrough following the release of V3 that completely changed the game in video generation compared to other tools like Clling, Luma, and Sora. But now, Nano Banana is making a similar level of impact, but now in comparison to existing tools like midjourney, Dolly and Sable Dusion, which are all already impressive in themselves.
While Nanto Banana still have some minor improvements to be made, having the experience in using tools like Nanto Banana will not only help you save time in photo editing, but also give you more lateral experience that helps you in your workspace. So, let's hop on over to the lab section to actually try this out in practice.
All right, let's start with the labs. In this lab, we're testing Google's Gemini 2.5 flash image preview model, known by its code name Nano Banana, which was just released on August 26th, 2025. The model made headlines after appearing anonymously on Ella Marina for weeks, dominating the image editing leaderboards before Google officially revealed it in their latest breakthrough.
What makes Nano Manennena significant is the ability to maintain character consistency across edits. Something that has been a major challenge for AI image tools. While other models often distort faces or backgrounds during simple edits, this model keeps subjects looking true to life through multiple transformations.
In our first task, we set up the Python environment and generate our initial image. We're asked to create a beautiful sunset over mountains with vibrant orange and purple hues. This demonstrates the model's core capability of transforming natural language description into photorealistic images. [Music]
Next, we explore the analysis features. In this question, we're asked to use Gemini's vision capabilities to analyze the sunset image we're just created. The model encodes the image to base 64 format and provides detailed analysis of colors, mood, and composition. This birectional functionality between generation and analysis sets Gemini apart from singlepurpose tools.
The editing capabilities showcase the real power of this system. In this question, we're asked to transform our sunset scene into a nighttime scene with stars and a moon. This demonstrates the targeted transformation abilities where you can make precise local edits using natural language prompts while maintaining the core composition. [Music]
We also encounter a multiple choice question about the main capabilities of the model which covers image generation from text vision analysis, image editing and multimodal understanding.
Finally, we execute a complete workflow pipeline. In this question, we're asked to run a generate edit analyze sequence creating a serene lake scene, adding a rainbow to enhance it, and then analyze the final results. This shows how these capabilities work together seamlessly for complete creative workflows.
The technical implementation runs through the Gemini API at about 4 cents per image with access available through Google AI Studio and Vert.Ex AI for developers or directly through the Gemini app for everyday users. According to the current LM Marina benchmarks, this is the top rated image editing model worldwide. This represents Google's strategic move to compete with OpenAI in the creative AI space, offering precise creative control through natural language interfaces rather than the inconsistent results typically of earlier AI image tool. [Music]