Weeks ago, a model codenamed after the enigmatic “Banana” made a quiet debut on evaluation platforms—no announcements, no official documentation. Yet, it stunned the AI community by outperforming a host of established models with its remarkable image quality and character consistency, creating a sensation.
At the time, speculation ran rife: some guessed it was a secret experiment by OpenAI, while others thought it might be a “dark horse” creation from an independent research team. Then, in late August, the mystery was solved—Google stepped forward to claim it: Nano Banana is none other than Google’s newly released text-to-image model, Gemini 2.5 Flash Image.
As an upgrade to Gemini 2.0 Flash, Nano Banana functions as an AI editor more aligned with real-world workflows. It not only maintains high consistency in characters and visuals across multiple edits but also enables users to make precise local modifications and multi-image compositions using simple natural language. Unlike most previous models, which focused solely on “generating a single good image,” Nano Banana acts more like an on-call design assistant—helping users iterate, adjust, refine, and create continuously. After testing it, numerous netizens commented that this could mark the end of the Photoshop era.
Against the backdrop of an intensely competitive text-to-image model landscape, what makes Nano Banana stand out and spark another wave of excitement? How does it differ from formidable rivals like OpenAI and Flux? What is its actual performance like? And where does Google’s multimodal capability stand today?
Nano Banana: A “Sudden Rising Star”
Before Google officially claimed ownership, Nano Banana made an anonymous appearance on LMArena—the world’s current most popular and authoritative large model evaluation platform. Operating as a community-voted AI model “arena,” LMArena pits two anonymous models against each other; users cast “blind votes” for their preferred results, and the platform ranks models using algorithms based on these community votes.
Around mid-August, users began noticing a mysterious, unfamiliar model codename—Nano Banana—on LMArena’s text-to-image and image editing leaderboards. In the following days, it soared rapidly through the rankings with its exceptionally stable and stunning outputs, eventually securing the top spot.
Nano Banana: Capabilities and Feedback
The last time the text-to-image model space saw such excitement was during the GPT-4o “Studio Ghibli craze” a few months prior. So, what makes Nano Banana so impressive? We spoke to several developers, who all highlighted one key breakthrough: its consistency.
In the past, a common flaw of many models during repeated image edits was that “changing clothes would also alter the character’s face.” For example, if you tried to change the color of a jacket in a photo, the system might inadvertently distort the facial features. These small “inconsistencies” made it hard for users to rely on AI as a credible creative tool. Nano Banana addresses this by retaining the core features of characters or objects throughout multiple editing rounds. Whether adjusting poses, changing clothing, or placing a dog in a new background, the main subject remains unchanged.
Its second major breakthrough lies in multi-image fusion. Previously, combining two entirely different photos often resulted in inconsistencies between images, spatial distortions, lost details, or deformations—with human figures frequently looking “tacked on.” Nano Banana, however, automatically ensures stylistic and logical consistency during fusion, creating a seamless, unified visual effect.
The third highlight is precise modifications driven by natural language. In the past, editing a photo required users to draw masks or repeatedly touch up with professional tools. Now, simple descriptions suffice: “Change the background,” “Remove the person from the photo,” “Adjust the character’s pose”—Nano Banana executes these requests accurately while keeping other elements intact, lowering the barrier to image editing to nearly zero. It even supports non-verbal interaction: you can simply sketch a doodle to convey your needs.
Additionally, Nano Banana integrates multi-turn conversational editing and style blending. You can first ask it to paint a room mint green, then add a bookshelf and replace the carpet—and the model will remember the context step-by-step without undoing previous edits. You can even request creative styles, such as applying petal textures to shoes or transforming butterfly wing patterns into a dress, generating entirely new, innovative designs.
Of course, security is also a priority. Google adds visible watermarks to all images generated by Nano Banana, along with SynthID—an invisible digital watermark—to enable future identification and tracing of AI-generated content.
Five Core Pillars: The Explosion of Google’s Multimodal Ecosystem
Viewed through a longer timeline, Nano Banana is not an “accidental breakthrough” for Google. Over the past year and more, Google has launched multimodal products at a “blitzkrieg” pace, with a dizzying array of models and iterations. Today, Google’s multimodal product lineup has evolved into a comprehensive matrix, roughly divided into five core pillars.
Pillar 1: Imagen Series (Text-to-Image)
The Imagen series traces its roots to May 2022, when Google Research first introduced this text-to-image model. Its defining feature was combining large language models (LLMs) for prompt understanding with diffusion models for image generation—even then, it was regarded as a next-generation solution surpassing DALL·E 2. However, due to safety and copyright concerns, Imagen was not initially made available to the public. It wasn’t until 2024 that Google officially launched Imagen 3, marking its transition to a commercial product. By May 2025, Imagen 4 was released, further enhancing lighting effects and detail quality, moving closer to “photorealism.”
Pillar 2: Veo Series (Text-to-Video)
In January 2024, Google Research unveiled Lumiere, which generates full video clips directly using “spatiotemporal consistent diffusion,” ensuring smoother movements and backgrounds. Then, at the May 2024 I/O Conference, Veo 1 made its official debut, capable of generating 1080p high-definition videos. By December 2024, Veo 2 was upgraded to 4K resolution and integrated into the Vertex AI platform for the first time. At the May 2025 I/O Conference, Google launched Veo 3—now capable of generating not just videos, but also synchronized music and voiceovers, advancing text-to-video into the era of film-grade creation.
Pillar 3: Genie Series (“Interactive World Generation” / “World Models”)
Unlike text-to-video models, Genie’s goal is not to create “viewable” videos, but to generate “playable” virtual worlds. Genie 1, launched in early 2024, was the first model capable of generating playable 2D game environments from images, showcasing AI’s potential to create interactive worlds. It was followed by Genie 2 in late 2024, which expanded AI-generated virtual environments from 2D to 3D spaces. The latest iteration, Genie 3, released on August 5 this year, takes capabilities to new heights: it can generate dynamic, navigable 3D worlds from text or image prompts, and for the first time supports real-time interaction and “prompt-driven world events”—allowing users to modify objects or weather in the generated environment in real time. This makes Genie a unique branch of Google’s multimodal matrix: it combines text-to-video with virtual interaction, signaling that Google’s multimodal exploration is pushing the boundaries of “immersive experiences” and “virtual world construction.”
Pillar 4: Creator-Centric Toolkits
In May 2024, Google launched ImageFX and VideoFX simultaneously at its I/O Conference, letting users experience text-to-image and text-to-video capabilities directly in Labs. By May 2025, Google released Flow—a tool specifically designed for film narrative, integrating the capabilities of Veo and Imagen into workflows for storyboarding, shot composition, and narrative styling.
Pillar 5: Gemini Multimodal Foundation Model
Gemini serves as Google’s general-purpose multimodal foundation model—the “brain” of the entire system. Its core capability lies in understanding, reasoning, and processing diverse data types, including text, images, audio, and video. Acting as a general intelligent agent, Gemini provides robust foundational support and world knowledge to other specialized models.
When compiling this lineup, Google’s multimodal strategy becomes increasingly clear: Imagen for text-to-image, Veo for text-to-video, Genie for exploring interactive worlds, and tools like Flow, ImageFX, and VideoFX to embed these capabilities into creative workflows—all backed by the rapidly evolving Gemini multimodal foundation. Meanwhile, Google is gradually advancing toward building a “comprehensive and all-encompassing” intelligent agent.
Looking ahead, there is widespread speculation that Google may integrate more model capabilities into Gemini, creating a multimodal “super traffic portal” for ordinary users. From Nano Banana to a full-fledged multimodal matrix, we have witnessed Google’s accelerated breakthroughs over the past year and more. In the generative AI race, Google was once questioned for falling behind. But now, it has nearly filled every gap—whether in images, videos, virtual worlds, or creative workflows.
This “one-two punch” style of product launches seems to send a clear message to the world: Google is not just catching up—it is attempting to redefine the boundaries of generative AI with a comprehensive matrix.
