Data-Driven Insights
Our analysis is based on real ChatGPT query data collected through voluntary opt-in participation, while fully complying with privacy requirements. This gives us unique visibility into actual user preferences rather than speculation.
I haven't seen this type of data published elsewhere, and I thought it could be interesting to share with the community. The insights are readily available in our dataset, providing a rare glimpse into real-world ChatGPT model usage patterns.
Live Model Usage Dashboard
Here's an overview of the breakdown of queries by model, updated in real-time from our data collection:
Key Findings
GPT-4o
Leading Model
Dominates user preferences with its balanced performance and accessibility
o3
Strong Second
Rapidly gaining adoption among users who experience its superior capabilities
GPT-4.1
Surprising Usage
Unexpected usage patterns - more adoption than initially anticipated
What the Data Tells Us
GPT-4o leads as expected - This aligns with our expectations given its widespread availability and balanced performance profile. It serves as the workhorse model for most users' daily interactions.
o3 shows strong adoption - Coming in second place, the o3 model demonstrates significant user adoption. I wonder if every user has experienced how much better o3 is compared to GPT-4o? The data suggests that those who have tried it are sticking with it for their more demanding tasks.
GPT-4.1 surprises with meaningful usage - I'm genuinely surprised by GPT-4.1's presence in the data. I thought literally no one used this model, but the numbers tell a different story. This highlights how user behavior can differ from our assumptions.
Implications for Users and Developers
GPT-4o Dominance
GPT-4o leads the pack as expected, proving its effectiveness as the go-to model for most users
o3's Rapid Rise
The o3 model shows strong adoption, likely driven by users who have experienced its superior reasoning capabilities
GPT-4.1 Surprise
GPT-4.1 shows unexpected usage - demonstrating that even seemingly lesser-known models have their user base
Quality Over Quantity
Users appear to gravitate towards models that deliver superior performance, even if they're newer
The Power of Real Data
This analysis demonstrates the value of examining actual usage patterns rather than relying on assumptions or marketing materials. The data reveals nuanced user preferences that might not be immediately obvious.
What's particularly interesting is how the data challenges some of our preconceptions - like the unexpected usage of GPT-4.1, or the rapid adoption rate of the newer o3 model despite its recent introduction.
What's Next?
More Data Breakdowns Coming
If this sort of data analysis is helpful to the community, we can provide more detailed breakdowns including usage patterns by time, geographic distribution, query types, and model performance comparisons.
We're committed to sharing insights that help the AI community better understand real-world usage patterns. This data can inform everything from product decisions to research priorities.
What other aspects of ChatGPT usage would you find valuable to explore? Let us know what data breakdowns would be most useful for your work or research.
Stay Updated on AI Usage Trends
Get access to more data-driven insights about AI model usage, search patterns, and emerging trends.
Ben Tannenbaum
Founder of Aiso, focused on AI search optimization and data-driven insights. Passionate about understanding how AI is changing search behavior and helping businesses adapt to this new landscape.
Learn more about Ben →