Drug combinations are increasingly studied in the field of anticancer agents. Mathematical models, such as Loewe, Bliss, and HSA, are used to interpret drug combinations, while informatics tools help cancer researchers identify the most effective combinations. However, the different algorithms each software uses lead to results that do not always correlate. This study compared the performance of Combenefit (Ver. 2.021) and SynergyFinder (Ver. 3.6) in analyzing drug synergy by studying combinations involving non-steroidal analgesics (celecoxib and indomethacin) and antitumor drugs (carboplatin, gemcitabine, and vinorelbine) on two canine mammary tumor cell lines. The drugs were characterized, their optimal concentration–response ranges were determined, and nine concentrations of each drug were used to make combination matrices. Viability data were analyzed under the HSA, Loewe, and Bliss models. Celecoxib-based combinations showed the most consistent synergistic effect among software and reference models. Combination heatmaps revealed that Combenefit gave stronger synergy signals, while SynergyFinder produced better concentration–response fitting. When the average values of the combination matrices were compared, some combinations shifted from synergistic to antagonistic due to differences in the curve fitting. We also used a simulated dataset to normalize each software’s synergy scores, finding that Combenefit tends to increase the distance between synergistic and antagonistic combinations. We conclude that concentration–response data fitting biases the direction of the combination (synergistic or antagonistic). In contrast, the scoring from each software increases the differences among synergistic or antagonistic combinations in Combenefit when compared to SynergyFinder. We strongly recommend using multiple reference models and reporting complete data analysis for synergy claiming in combination studies.