Over the weekend, a small Chinese hedge fund turned star AI research outfit launched DeepSeek R1, a new massive open-weights model with state-of-the-art performance, trained on a shoestring budget.

Just how much interest is there in this advance?

I analyzed R1 downloads on Ollama, and I recorded my steps to perform this analysis with AI using speech, an AI model, & a developer environment. See the video below if you’re curious how I did it.

As the chart above shows, there’s a lot of interest. R1 tops the charts in terms of daily downloads.

It’s still relatively early though in terms of overall downloads. And of course, all model download patterns follow a decay function with most of the interest occurring at the beginning. Many of these models are weeks older. Some like Gemma & Phi are small models ; others like Llama3.3 include much larger versions.

Two implications emerge from the R1 news :

First, this innovation comes on the heels of a Christmas launch of Deepseek’s v3 model which prioritized latency, shows that the overall pace of innovation in AI presses forward unabated.

Second, R1’s technical approach highlights an emerging bifurcation in the AI model landscape. The team’s use of quantization - a sophisticated compression technique that maintains 90-95% accuracy - points to a future with two distinct model categories:

• High-speed, compressed models optimized for immediate tasks like table reformatting & quick analysis
• Research-oriented models built for complex, multi-step reasoning (similar to Gemini’s Deep Research)

R1 is a reasoning model. It’s chatty nature means it explicitly reasons & makes its plans clear to the user. For work that might take 10-15 minutes, this technique should reduce errors. It’s similar to Gemini’s Deep Research model.

The launch of DeepSeek R1 reinforces two key trends in AI: the rapid pace of innovation & the emerging split between fast, lightweight models & more deliberate reasoning models. Looking at the download data, the market shows clear interest in both approaches.

Here’s a step-by-step video on how I assembled this analysis.