Be a part of our every day and weekly newsletters for the newest updates and unique content material on industry-leading AI protection. Be taught Extra
DeepSeek-R1 has absolutely created a number of pleasure and concern, particularly for OpenAI’s rival mannequin o1. So, we put them to check in a side-by-side comparability on a number of easy knowledge evaluation and market analysis duties.
To place the fashions on equal footing, we used Perplexity Professional Search, which now helps each o1 and R1. Our purpose was to look past benchmarks and see if the fashions can really carry out advert hoc duties that require gathering info from the net, choosing out the appropriate items of information and performing easy duties that might require substantial handbook effort.
Each fashions are spectacular however make errors when the prompts lack specificity. o1 is barely higher at reasoning duties however R1’s transparency provides it an edge in instances (and there will probably be fairly a number of) the place it makes errors.
Here’s a breakdown of some of our experiments and the hyperlinks to the Perplexity pages the place you possibly can evaluation the outcomes your self.
Calculating returns on investments from the net
Our first take a look at gauged whether or not fashions may calculate returns on funding (ROI). We thought-about a state of affairs the place the person has invested $140 within the Magnificent Seven (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, Tesla) on the primary day of each month from January to December 2024. We requested the mannequin to calculate the worth of the portfolio on the present date.
To perform this process, the mannequin must pull Magazine 7 value info for the primary day of every month, cut up the month-to-month funding evenly throughout the shares ($20 per inventory), sum them up and calculate the portfolio worth in line with the worth of the shares on the present date.
On this process, each fashions failed. o1 returned an inventory of inventory costs for January 2024 and January 2025 together with a method to calculate the portfolio worth. Nonetheless, it didn’t calculate the proper values and mainly mentioned that there could be no ROI. Alternatively, R1 made the error of solely investing in January 2024 and calculating the returns for January 2025.
Nonetheless, what was fascinating was the fashions’ reasoning course of. Whereas o1 didn’t present a lot particulars on the way it had reached its outcomes, R1’s reasoning traced confirmed that it didn’t have the proper info as a result of Perplexity’s retrieval engine had didn’t acquire the month-to-month knowledge for inventory costs (many retrieval-augmented technology purposes fail not due to the mannequin lack of skills however due to dangerous retrieval). This proved to be an vital little bit of suggestions that led us to the subsequent experiment.
Reasoning over file content material
We determined to run the identical experiment as earlier than, however as an alternative of prompting the mannequin to retrieve the data from the net, we determined to offer it in a textual content file. For this, we copy-pasted inventory month-to-month knowledge for every inventory from Yahoo! Finance right into a textual content file and gave it to the mannequin. The file contained the identify of every inventory plus the HTML desk that contained the worth for the primary day of every month from January to December 2024 and the final recorded value. The information was not cleaned to cut back the handbook effort and take a look at whether or not the mannequin may decide the appropriate elements from the information.
Once more, each fashions failed to offer the appropriate reply. o1 appeared to have extracted the information from the file, however advised the calculation be accomplished manually in a software like Excel. The reasoning hint was very obscure and didn’t comprise any helpful info to troubleshoot the mannequin. R1 additionally failed and didn’t present a solution, however the reasoning hint contained a number of helpful info.
For instance, it was clear that the mannequin had accurately parsed the HTML knowledge for every inventory and was capable of extract the proper info. It had additionally been capable of do the month-by-month calculation of investments, sum them and calculate the ultimate worth in line with the newest inventory value within the desk. Nonetheless, that last worth remained in its reasoning chain and didn’t make it into the ultimate reply. The mannequin had additionally been confounded by a row within the Nvidia chart that had marked the corporate’s 10:1 inventory cut up on June 10, 2024, and ended up miscalculating the ultimate worth of the portfolio.
Once more, the true differentiator was not the consequence itself, however the means to analyze how the mannequin arrived at its response. On this case, R1 supplied us with a greater expertise, permitting us to know the mannequin’s limitations and the way we will reformulate our immediate and format our knowledge to get higher outcomes sooner or later.
Evaluating knowledge over the net
One other experiment we carried out required the mannequin to check the stats of 4 main NBA facilities and decide which one had the most effective enchancment in area purpose share (FG%) from the 2022/2023 to the 2023/2024 seasons. This process required the mannequin to do multi-step reasoning over totally different knowledge factors. The catch within the immediate was that it included Victor Wembanyama, who simply entered the league as a rookie in 2023.
The retrieval for this immediate was a lot simpler, since participant stats are extensively reported on the net and are often included of their Wikipedia and NBA profiles. Each fashions answered accurately (it’s Giannis in case you have been curious), though relying on the sources they used, their figures have been a bit totally different. Nonetheless, they didn’t understand that Wemby didn’t qualify for the comparability and gathered different stats from his time within the European league.
In its reply, R1 supplied a greater breakdown of the outcomes with a comparability desk together with hyperlinks to the sources it used for its reply. The added context enabled us to right the immediate. After we modified the immediate specifying that we have been in search of FG% from NBA seasons, the mannequin accurately dominated out Wemby from the outcomes.
Ultimate verdict
Reasoning fashions are highly effective instruments, however nonetheless have a methods to go earlier than they are often absolutely trusted with duties, particularly as different parts of huge language mannequin (LLM) purposes proceed to evolve. From our experiments, each o1 and R1 can nonetheless make fundamental errors. Regardless of displaying spectacular outcomes, they nonetheless want a little bit of handholding to provide correct outcomes.
Ideally, a reasoning mannequin ought to be capable to clarify to the person when it lacks info for the duty. Alternatively, the reasoning hint of the mannequin ought to be capable to information customers to higher perceive errors and proper their prompts to extend the accuracy and stability of the mannequin’s responses. On this regard, R1 had the higher hand. Hopefully, future reasoning fashions, together with OpenAI’s upcoming o3 collection, will present customers with extra visibility and management.
