  General large language models enhanced with supervised fine-tuning and
reinforcement learning from human feedback are increasingly popular in academia
and industry as they generalize foundation models to various practical tasks in
a prompt manner. To assist users in selecting the best model in practical
application scenarios, i.e., choosing the model that meets the application
requirements while minimizing cost, we introduce A-Eval, an application-driven
LLMs evaluation benchmark for general large language models. First, we
categorize evaluation tasks into five main categories and 27 sub-categories
from a practical application perspective. Next, we construct a dataset
comprising 678 question-and-answer pairs through a process of collecting,
annotating, and reviewing. Then, we design an objective and effective
evaluation method and evaluate a series of LLMs of different scales on A-Eval.
Finally, we reveal interesting laws regarding model scale and task difficulty
level and propose a feasible method for selecting the best model. Through
A-Eval, we provide clear empirical and engineer guidance for selecting the best
model, reducing barriers to selecting and using LLMs and promoting their
application and development. Our benchmark is publicly available at
https://github.com/UnicomAI/DataSet/tree/main/TestData/GeneralAbility.
