Understanding long-form videos, such as movies and TV episodes ranging from tens of minutes to two hours, remains a major challenge for multi-modal models. Existing benchmarks often fall short in testing the full range of cognitive skills needed to process these temporally rich and narratively complex inputs. We introduce {\papernameAbbrev}, a comprehensive benchmark designed to rigorously evaluate the capabilities of models in long video understanding. InfiniBench offers:(1) Over 1,000 hours of video content, with an average video length of 52.59 minutes, (2) The largest set of question-answer pairs for long video comprehension, totaling around \totalSampleNumber, (3) Eight diverse skills that span both grounding-based (e.g., scene transitions, character actions) and reasoning-based (e.g., deep context, multi-event linking) understanding, and (4) Rich annotation formats, including both multiple-choice and open-ended questions. We conduct an in-depth evaluation across both commercial (GPT-4o, Gemini 1.5 Flash) and open-source (Qwen2.5-VL, InternVL2.5) vision-language models. Results reveal that current models remain far from solving long video understanding: on grounding-based skills, the top open-source model (Qwen2.5-VL) and GPT-4o achieve only 39.4\% and 48.1\% accuracy, respectively. Interestingly, several models achieve non-trivial performance using only the movie or episode title, without watching the video, revealing a reliance on pre-trained world knowledge that partially compensates for the absence of visual or temporal understanding. These findings highlight critical gaps in current approaches and underscore the need for models that truly engage with long visual narratives.