C-Eval

Adoption judgment: C-Eval is a widely recognized benchmark for Chinese LLMs, but its leaderboard rankings are often misinterpreted as holistic model superiority. Actual function: It provides a standardized testbed covering humanities, science, and engineering subjects, enabling developers to identify domain-specific weaknesses and track progress during pretraining and fine-tuning. It should be used together with other benchmarks for a full evaluation. Threshold and cost: The dataset and evaluation scripts are fully open-source (GitHub: SJTU-LIT/ceval), downloadable at no cost. Running evaluations requires basic Python skills and GPU resources; a full evaluation on a large model may incur cloud GPU costs conservatively estimated at hundreds to low thousands of RMB. Target audience: Suited for academic labs, enterprise R&D teams developing Chinese LLMs, and model comparison studies; not suited for non-technical decision-makers seeking a single-score purchase decision, nor for assessing safety, multi-turn dialogue, or code capability.