# UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos

arXiv: https://arxiv.org/abs/2601.22162 · HTML: https://mlsanity.com/paper/2601.22162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- Multimodal LLMs / vision-language models / [Chart understanding and anomaly detection](https://mlsanity.com/mllm/chart-understanding-and-anomaly-detection/)

## Builds on (most cited first, 28 of 28)

- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension](https://mlsanity.com/paper/2404.16790/) (2024)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [A Survey of Large Language Models in Finance (FinLLMs)](https://mlsanity.com/paper/2402.02315/) (2024)
- [DocFinQA: A Long-Context Financial Reasoning Dataset](https://mlsanity.com/paper/2401.06915/) (2024)
- [MM-Vet v2: A Challenging Benchmark to Evaluate Large Multimodal Models for Integrated Capabilities](https://mlsanity.com/paper/2408.00765/) (2024)
- [FinTextQA: A Dataset for Long-form Financial Question Answering](https://mlsanity.com/paper/2405.09980/) (2024)
- [AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework](https://mlsanity.com/paper/2403.12582/) (2024)
- [FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains](https://mlsanity.com/paper/2311.09797/) (2023)
- [Financial Statement Analysis with Large Language Models](https://mlsanity.com/paper/2407.17866/) (2024)
- [FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation](https://mlsanity.com/paper/2505.24714/) (2025)
- [FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis](https://mlsanity.com/paper/2308.01430/) (2023)
- [Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation](https://mlsanity.com/paper/2309.08793/) (2023)
- [Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset](https://mlsanity.com/paper/2405.10542/) (2024)
- [Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2509.11420/) (2025)
- [XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning](https://mlsanity.com/paper/2508.15861/) (2025)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [StockGPT: A GenAI Model for Stock Prediction and Trading](https://mlsanity.com/paper/2404.05101/) (2024)
- [FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging](https://mlsanity.com/paper/2508.04625/) (2025)
- [RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data](https://mlsanity.com/paper/2404.07452/) (2024)
- [DocTabQA: Answering Questions from Long Documents Using Tables](https://mlsanity.com/paper/2408.11490/) (2024)
- [VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding](https://mlsanity.com/paper/2508.09641/) (2025)
- [VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations](https://mlsanity.com/paper/2507.08104/) (2025)
- [A Survey on Open Dataset Search in the LLM Era: Retrospectives and Perspectives](https://mlsanity.com/paper/2509.00728/) (2025)
- [Unlocking Financial Insights: An advanced Multimodal Summarization with Multimodal Output Framework for Financial Advisory Videos](https://mlsanity.com/paper/2509.20961/) (2025)
- [FinCap: Topic-Aligned Captions for Short-Form Financial YouTube Videos](https://mlsanity.com/paper/2509.25745/) (2025)
- [FinMR: A Knowledge-Intensive Multimodal Benchmark for Advanced Financial Reasoning](https://mlsanity.com/paper/2510.07852/) (2025)

## Abstract (arXiv)

Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-density information and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing multimodal benchmarks. To address this gap, we propose UniFinEval, the first unified multimodal benchmark designed for high-information-density financial environments, covering text, images, and videos. UniFinEval systematically constructs five core financial scenarios grounded in real-world financial systems: Financial Statement Auditing, Company Fundamental Reasoning, Industry Trend Insights, Financial Risk Sensing, and Asset Allocation Analysis. We manually construct a high-quality dataset consisting of 3,767 question-answer pairs in both chinese and english and systematically evaluate 10 mainstream MLLMs under Zero-Shot and CoT settings. Results show that Gemini-3-pro-preview achieves the best overall performance, yet still exhibits a substantial gap compared to financial experts. Further error analysis reveals systematic deficiencies in current models. UniFinEval aims to provide a systematic assessment of MLLMs' capabilities in fine-grained, high-information-density financial environments, thereby enhancing the robustness of MLLMs applications in real-world financial scenarios. Data and code are available at https://github.com/aifinlab/UniFinEval.