Multimodal tables i.e. tabular layouts interleaved with charts, maps, icons, and color encodings are ubiquitous in real applications yet remain difficult for Multimodal Large Language Models (MLLMs). Despite advances in text and image understanding, systematic evaluation of table-centric multimodal reasoning is limited. We introduce MMTabReal, a MultiModal Table Benchmark, human-curated suite of 500 real-world tables paired with 4021 question–answer pairs. MMtabReal spans four question types, five reasoning categories, and eight structural archetypes. Evaluations of state-of-the-art models reveal substantial gaps, especially in visual grounding, spatial alignment, and multi-step inference, with 20–40% performance drops relative to existing benchmarks. These results highlight the need for architectures that more tightly fuse vision with tabular structure and support explicit numeric/logical operations. MMtabReal is released for evaluation only, providing a rigorous, reproducible testbed that reflects the linguistic, structural, and reasoning complexity of real-world multimodal tables. Code and data are available at: https://coral-lab-asu.github.io/mmtabreal/

Multimodal Large Language Models struggle with tabular reasoning on tables containing images as cells owing to their limited memory and the distractions posed by the surrounding irrelevant visual elements. Existing context-reduction methods reduce context size but degrade reasoning accuracy: text-only pruning neglects visual cells, token-level pruning discards valuable information, and retrieval cannot understand table structure. We propose CAPTR: Caption-based Context Pruning for Tabular Reasoning, a two-stage framework that uses image captions as a lightweight textual proxy to prune non-pertinent rows and columns and then restores images for reasoning. CAPTR can reduce the context length by 80% while achieving up to 8% accuracy improvement against competitive baselines. By analyzing the error cases, we find that entity confusion contributes to the majority of the remaining errors. CAPTR is the first multimodal context-reduction method to improve performance on multimodal tabular reasoning.