InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows

TL;DR

InfiniBench is a comprehensive benchmark with extensive long-form video content and diverse questions, designed to evaluate and challenge multi-modal models' ability to understand complex, narratively rich videos.

Contribution

This paper introduces InfiniBench, the largest and most diverse benchmark for long video understanding, including extensive content, questions, and skills, to rigorously evaluate multi-modal models.

Findings

Models perform poorly on long video comprehension tasks.

Models rely heavily on metadata and world knowledge.

Multimodal input significantly improves model performance.

Abstract

Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a significant challenge for multi-modal models. Existing benchmarks often fail to test the full range of cognitive skills needed to process these temporally rich and narratively complex inputs. Therefore, we introduce InfiniBench, a comprehensive benchmark designed to evaluate the capabilities of models in long video understanding rigorously. InfiniBench offers:(1) Over 1,000 hours of video content, with an average video length of 53 minutes. (2) The largest set of question-answer pairs for long video comprehension, totaling around 87.7 K. (3) Eight diverse skills that span both grounding-based (e.g., scene transitions, character actions) and reasoning-based (e.g., deep context understanding, multi-event linking). (4) Rich annotation formats, including both multiple-choice and open-ended questions. We conducted an in-depth evaluation across both commercial (GPT-4o, Gemini 2.0 Flash) and most recent open-source vision-language models such as Qwen2.5-VL, InternVL3.0). Results reveal that:(1) Models struggle across the board: Even the best model, GPT-4o, achieves only 47.1 % on grounding-based skills, with most models performing near or just above random chance. (2) Strong reliance on world knowledge: Models achieve surprisingly high scores using only metadata (e.g., video titles), highlighting a tendency to rely on pre-trained knowledge rather than actual visual or temporal understanding. (3) Multi-Modal Importance: When provided with full video and subtitle context, however, models show substantial improvements, confirming the critical role of multimodal input in video understanding. InfiniBench is publicly available at https://vision-cair.github.io/Infinibench