Multimodal large language models (MLLMs) have shown increasing potential for applications in the financial domain. However, the challenges posed by real-world financial scenarios, including high-information-density, strong noise interference, and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing financial multimodal benchmarks. To this end, we propose UniFinEval, the first unified multimodal financial evaluation benchmark designed for high-density financial information environments, covering three modalities: text, image, and video. UniFinEval abstracts three progressive core scenarios from perception to reasoning and decision-making, and further refines them into six task categories. It contains 5,567 high-quality Chinese and English question–answer (QA) pairs constructed by financial experts. We systematically evaluate 10 mainstream MLLMs under zero-shot and chain-of-thought (CoT) settings. Experimental results show that Gemini-3-Pro achieves the best overall performance, but still exhibits a significant gap compared with financial experts. Further error analysis reveals the capability deficiencies of MLLMs in financial scenarios. UniFinEval aims to systematically evaluate the capabilities of MLLMs in real financial scenarios, thereby promoting the application of MLLMs in the financial domain. The related code and data are available at https://github.com/aifinlab/UniFinEval.