Image model releases are driving growth for AI mobile apps, generating 6.5 times more downloads than traditional model updates, according to a new report from app intelligence provider Appfigures. This marks a shift from earlier days, when the release of new models powering conversational experiences drove more demand, alongside features like voice chat interfaces. For instance, ChatGPT and Gemini each added tens of millions of new downloads after releasing their respective image models, Appfigures found.
Image models outperform chatbot upgrades
For Google’s Gemini, the release of its image model Nano Banana drove an additional 22+ million downloads in the 28 days following the introduction of the Gemini 2.5 Flash image model last August. This launch lifted the app’s downloads by more than four times over that period, the data showed. Meanwhile, ChatGPT added more than 12 million incremental installs in the 28 days after the introduction of its GPT-4o image model in March of last year. That’s roughly 4.5 times more downloads than it saw for its GPT-4o, GPT-4.5, and GPT-5 model releases, Appfigures pointed out.
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Other model releases followed similar trends, though on a smaller scale. Meta AI’s introduction of its AI video feed Vibes added an estimated 2.6 million incremental downloads in the 28 days after its September 2025 release. While technically a video model, it’s ultimately about visual content, not just text.
Downloads don’t always translate to revenue
Still, the report cautioned, additional downloads don’t always translate into increased mobile revenue. New image model releases give people a reason to install the app and try out its improved image generation capabilities. That doesn’t mean they’ll necessarily convert to paying users. For example, Appfigures noted that Nano Banana drove only $181,000 in estimated gross consumer spending during the 28-day window following its release, even though it produced a larger spike in downloads than ChatGPT’s 4o image model release. Meta AI’s launch of Vibes also led to additional downloads, but no meaningful revenue.
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ChatGPT bucks the revenue trend
Among the three, only ChatGPT turned the increased attention into actual dollars. OpenAI’s 4o image generation model led to an estimated $70 million in gross consumer spending over the 28 days after its launch, compared with its prior baseline, Appfigures said. This suggests that while image models are effective at driving initial interest, monetization depends on factors like user engagement, subscription models, and perceived value.
DeepSeek: A different pattern
The company also looked at DeepSeek in its analysis, but it didn’t fit the pattern. While DeepSeek R1 drove 28 million downloads after its January 2025 release, it wasn’t a typical model comparison event. This was DeepSeek’s breakout moment, when it went from being relatively unknown to an overnight sensation as the tech industry learned about the techniques it used to train its AI models at a fraction of the cost of its competitors. This case highlights how curiosity can drive downloads — though in this instance, the interest wasn’t tied to an image model.
Conclusion
Image AI models are proving to be powerful drivers of app growth, outperforming chatbot upgrades in generating downloads. However, the report underscores that increased downloads don’t automatically lead to higher revenue. For developers and marketers, the key takeaway is that while visual AI features can attract users, converting them into paying customers requires a strategic approach to monetization and user retention.
FAQs
Q1: Why are image models driving more downloads than chatbot upgrades?
Image models offer a visually engaging and shareable experience that attracts users to try the app, often leading to viral growth. Chatbot upgrades, while useful, may not generate the same level of curiosity or excitement.
Q2: Does increased app downloads always mean more revenue?
No, as shown in the report, only ChatGPT saw a significant revenue boost from its image model release. Other apps experienced download spikes but minimal additional spending, indicating that user conversion depends on other factors like pricing and perceived value.
Q3: How does DeepSeek’s case differ from the image model trend?
DeepSeek’s downloads were driven by curiosity about its cost-efficient AI training methods, not by an image model release. This highlights that different factors can drive app growth, but the impact on revenue may vary.
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