As described above, ChatMolData has demonstrated proficiency in handling four types of tasks: database-related, molecular data structuring, prediction&visualization, and search tasks. To rigorously evaluate the performance of ChatMolData across these tasks, we designed 96 distinct tasks as benchmarks. These tasks varied across four dimensions, including the differences in the specified files, task subtypes, keyword and task-description. The file differences refer to input file formats, such as molecular structures provided in either MOL or SDF formats. Task subtypes had been introduced previously; for example, in database-related tasks, some involved exporting molecular information from databases, while others required importing existing data files into a database. The keyword variations reflect subtle differences in task details; for instance, predicting molecular properties could target either bioactivity against a specific target or the physicochemical properties of the compounds. Based on these distinctions, each task category ultimately produced 8 subtasks, culminating in a total of 32 tasks. Inspired by the work on ChatMOF , for each task above, GPT-4 was further utilized to generate three additional queries to express tasks with different descriptions (examples are shown in Supplementary Information Note 10). This step was designed to examine whether ChatMolData could comprehend descriptions from users with different expression habits, which finally resulted in 128 tasks overall. Given that existing automatic evaluation methods fail to capture the complexity of multimodal chemical data, all task outputs were evaluated by expert chemists . The experts assigned each result a score of either "True" or "False." A "True" score not only indicated that the output file and answer were satisfactory but also that the agent used the tools in an optimal and logical manner. Otherwise, the result is "False" As shown in Table , ChatMolData, integrated with GPT-4, exhibits excellent performance. For the database-related, molecular data structuring, Prediction & visualization, and search tasks, the accuracy was 90.6%, 87.5%, 96.9%, and 93.8%, respectively, leading to an overall accuracy of 92.2%. These results indicate that in the vast majority of tasks, ChatMolData was able to effectively and logically utilize various tools, making it a superior multimodal agent for handling multiple types of molecular data and delivering satisfactory molecular files and answers.