A comprehensive comparison of AI models DeepSeek-R1 and o1 reveals that while both systems make errors, R1’s transparent reasoning process gives it an advantage in practical applications. This finding comes from recent testing conducted by Ben Dickson, as reported in VentureBeat.
The comparison focused on real-world tasks including investment calculations, data analysis, and sports statistics research. Using Perplexity Pro Search as the testing platform, both models were evaluated on their ability to gather web information and perform complex analytical tasks.
In a notable investment calculation test, both models failed to correctly compute returns on a hypothetical investment in the “Magnificent Seven” tech stocks. However, R1’s detailed reasoning trace helped identify why the error occurred, pointing to issues with the data retrieval system rather than the model itself.
The models also attempted to analyze NBA player statistics, comparing field goal percentages across seasons. While both eventually reached correct conclusions, R1 provided more comprehensive documentation of its analysis process and data sources, allowing users to better understand and verify the results.
A key finding emerged regarding prompt specificity. Both models performed better when given precise instructions, but R1’s transparency made it easier to identify when and how to adjust prompts for improved results.
The testing revealed that current AI models still require careful human oversight for complex analytical tasks. However, R1’s transparent approach to showing its work provides valuable feedback for users to understand and correct potential errors.
Industry experts note that while these models show promise, their practical implementation requires understanding their limitations and providing clear, specific instructions to achieve reliable results.