Google DeepMind Developers: How Nano Banana Was Made
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What are the biggest challenges in AI vision that the hosts introduce?
These models are allowing creators to do um less tedious parts of the job, right? They can be more creative and they can spend, you know, ninety percent of their time being creative versus ninety percent of their time like editing things and doing these tedious kind of manual operations.
I'm convinced that this ultimately really empowers the artists, right? It gives you new tools, right? It's like, hey, we now have I don't know, watercolors for Michelangelo, let's see what he does with it, right? And amazing things come out.
One of the hardest challenges in AI isn't language or reasoning, it's vision. Getting models to understand, compose, and edit images with the same precision that they process text. Today you'll hear a conversation with Oliver Wang and Nicole Brichtova from Google Deepmind about Gemini 2.5 image, also known as Nanobanana. They discuss the architecture behind the model, how image generation and editing are integrated into Gemini's multimodal framework, and what it takes to achieve character consistency, compositional control, and conversational editing at scale. They also touch on open questions in model evaluation, safety, and latency optimization, and how visual reasoning connects to broader advances in multimodal systems.
Let's get into it.
Maybe start by telling us about the backstory behind the nano banana model. How did it come to be? How did y'all start working on it?
Sure. So our team has worked on image models for some time. We developed the Imagine family of models, which goes back a couple years. And actually there was also an an image generation model in Gemini before the Gemini two point oh image generation model. So what happened was the teams kind of started to focus more on the Gemini use cases. So like interactive, conversational and editing. And essentially what happened was we teamed up and we built this model, which became what's known as nano banana. So yeah, that's sort of the origin story. But Yeah, and I think maybe just some more background on that. So our imagine models were always kind of top of the charts for visual quality. And we really focused on kind of these specialized generation editing use cases.
And then when 2.0 Flash came out, that's when we really started to see some of the magic of being able to generate images and text at the same time. So you can maybe tell a story. Just the magic of being able to talk to images and edit them conversationally. But the visual quality Quality was maybe not where we wanted it to be. And so nano banana or Gemini two point five flash image. Nano banana is way cooler. It's easier to say. Easier to say. It's a lot easier to say. It's the
name that's stuck. Yes, it's the
name that's stuck. But it really became kind of the best of both worlds in that sense, like the Gemini smartness and the multimodal kind of conversational nature of it, plus the visual quality of imagine. And I feel like that's maybe what resonates a lot with people.
Oh, amazing. So I guess when you were testing out a model as you were developing it, what were some wow moments that you found? I know this is gonna go viral. I know people will love this.
So I actually didn't feel like it was going to go viral until we had released on Elamarina. And what we saw was that we budgeted a comparable amount of queries per second as we had for our previous models that were on LM Arena. And we had to keep upping that number as people were going to LM Arena to use the model. And I feel like that was the first time when I was really like, oh wow, this is something that's very, very useful to a lot of people. Like it surprised even me. I don't know about the whole team, but we were trying to make the best conversational editing model possible. But then it really started taking off when people were like going out of their way and using a website that would actually only give you the model some percentage of the time.
But even that was worth like going to that website to use the model. So I think that was really the moment, at least for me, that I was like, Oh wow, this is gonna be bigger.
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Chapters
6 chapters
1
What are the biggest challenges in AI vision that the hosts introduce?
0:00–4:46
2
How did the Nano Banana model originate and what was the development back‑story?
4:46–9:56
3
What were the surprise “wow” moments when testing Nano Banana and why did it go viral?
9:56–20:37
4
How does the team evaluate character consistency and visual quality in the model?
20:37–29:14
5
What UI design trade‑offs and control knobs are being considered for future image editors?
29:14–42:06
6
Will image generation move beyond pixels to new representations like 3‑D or SVG?
42:06–54:08
Speakers
5 identifiedMore from The a16z Show
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