Large language models are increasingly regarded as strong translators, yet it remains unclear what their success establishes beyond overall translation quality. This question is especially consequential in Chinese web novel translation, where sentence-level cultural meaning shapes tone, characterization, and reader experience. Existing evaluation provides only indirect evidence, as benchmarks emphasize overall quality and available metrics do not explicitly assess the cultural and pragmatic phenomena underlying reader experience. We introduce DITING (a mythological creature known for discerning truth), the first comprehensive evaluation framework for this problem, supported by an expert-annotated benchmark of over 18K Chinese-English sentence pairs, a reasoning-driven multi-agent evaluator, AgentEval, and a 1,440-pair meta-evaluation benchmark, MetricAlign. AgentEval achieves the strongest correlation with human judgments among eight representative automatic metrics. Applying DITING to 14 translation systems shows that current progress is selective rather than general: frontier LLMs lead overall, but their gains concentrate in a subset of dimensions, while zero-pronoun resolution remains the clearest bottleneck. Overall, our results show that aggregate rankings substantially overstate how broadly sentence-level cultural meaning has been solved in web novel translation. All the code and data in https://github.com/whunextgen/DITING.