Large language models (LLMs) have made significant progress in natural language processing tasks and demonstrate considerable potential in the legal domain. However, legal applications demand high standards of accuracy, reliability, and fairness. Applying existing LLMs to legal systems without careful evaluation of their potential and limitations could pose significant risks in legal practice.To this end, we introduce a standardized comprehensive Chinese legal benchmark CoLLaM. This benchmark is notable in the following three aspects: (1) Ability Modeling: We propose a new taxonomy of legal cognitive abilities to organize different tasks. (2) Scale: To our knowledge, CoLLaM is currently the largest Chinese legal evaluation dataset, comprising 23 tasks and 13,650 questions. (3) Data: we utilize formatted existing datasets, exam data and newly annotated data by legal experts to comprehensively evaluate the various capabilities of LLMs. CoLLaM not only focuses on the ability of LLMs to apply fundamental legal knowledge but also dedicates efforts to examining the ethical issues involved in their application. We evaluated 38 open-source and commercial LLMs and obtained some interesting findings. The experiments and findings offer valuable insights into the challenges and potential solutions for developing Chinese legal systems and LLM evaluation pipelines. The CoLLaM dataset and leaderboard are publicly available at https://github.com/CSHaitao/CoLLaM and will be continuously updated.