Recent advances in video generation have posed great challenges in the assessment of AI-generated content, particularly with the emergence of increasingly sophisticated models. The various inconsistencies and defects observed in such videos are inherently complex, making overall scoring notoriously difficult. In this paper, we emphasize the critical importance of integrating fine-grained reasoning into video evaluation. We propose FingER, a novel entity-level reasoning evaluation framework that first automatically generates Fine-grained Entity-level questions, and then answers those questions by a Reasoning model with scores, which can be subsequently weighted summed to an overall score for different applications. Specifically, we leverage LLMs to derive entity-level questions across five distinct perspectives, which (i) often focus on some specific entities of the content, thereby making answering or scoring much easier for MLLMs, and (ii) are more interpretable. Then we construct a FingER dataset, consisting of approximately 3.3k videos and corresponding 60k fine-grained QA annotations, each with detailed reasons. Based on that, we further investigate various training protocols to best incentivize the reasoning capability of MLLMs for correct answer prediction. Extensive experiments demonstrate that a reasoning model trained using GRPO with a cold-start strategy achieves the best performance. Notably, our model surpasses existing methods by a relative margin of 11.8% on GenAI-Bench and 5.5% on MonetBench with only 3.3k training videos, which is at most one-tenth of the training samples utilized by other methods. Our codes and datasets have been released.