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.

Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored, and their performance on complex tasks like financial agent remains unknown. This paper presents FinEval, a benchmark designed to evaluate LLMs’ financial domain knowledge and practical abilities. The dataset contains 8,351 questions categorized into four different key areas: Financial Academic Knowledge, Financial Industry Knowledge, Financial Security Knowledge, and Financial Agent. Financial Academic Knowledge comprises 4,661 multiple-choice questions spanning 34 subjects such as finance and economics. Financial Industry Knowledge contains 1,434 questions covering practical scenarios like investment research. Financial Security Knowledge assesses models through 1,640 questions on topics like application security and cryptography. Financial Agent evaluates tool usage and complex reasoning with 616 questions. FinEval has multiple evaluation settings, including zero-shot, five-shot with chain-of-thought, and assesses model performance using objective and subjective criteria. Our results show that Claude 3.5-Sonnet achieves the highest weighted average score of 72.9 across all financial domain categories under zero-shot setting. Our work provides a comprehensive benchmark closely aligned with Chinese financial domain. The data and the code are available at https://github.com/SUFE-AIFLMLab/FinEval.