Large-scale multimodal instruction data hides two types of toxic samples: visual redundancy (image doesn't matter) and image-text mismatch (image misleads the answer). VisNec, accepted at ECCV 2026, uses a plain but ruthless idea: run the same sample twice — once normally with multimodal forward, once replacing the image with a pad token and masking the corresponding attention, getting a text-only forward. The difference in their losses is "how much the image reduced uncertainty for this sample": greater than 0 is true cross-modal, approximately equal to 0 can be answered by language priors alone, less than 0 is image-text contradictory, dragging backward. The effect is astonishing: on LLaVA-665K, 15% of the data gets 100.2% of the full-data performance, on the more complex Vision-Flan-186K it surpasses full-data by 15.8%. The key is transferability — applying the scoring function to Qwen2.5-VL's 3B/7B/32B three scales, 15% data still reaches 103.8%, 104.0%, 102.4% of full-data, showing that what's being captured is the visual necessity of the data itself, not a particular model's preference. The implementation clusters instructions by question semantics into 20 categories, and within each category selects top-r% by VisNec score, ensuring both "the image is indeed useful" and task diversity. Overall fine-tuning time drops from 76 hours to 23 hours, about 3.3× speedup, just two forward passes, no extra training or external API needed. The data-scale superstition era is loosening. VisNec confirms one thing: the bottleneck of multimodal LLM isn't sample count, but how much work the image actually does in each sample. When "data filtering" moves from statistical heuristics to "causal-contribution-based scoring", the script of small data beating big data will repeat in more modality-fusion tasks.