Interpretability methods are increasingly invoked to diagnose Large Language Model (LLM) failures, but most evidence comes from short prompts or toy templates, leaving their utility on practical benchmarks unclear. We cast failure diagnosis as contrastive attribution over target–contrast token pairs, attributing the logit gap between incorrect and correct output tokens to input tokens and internal states, and introduce a batch-packed multi-target backpropagation scheme that scales cross-layer attribution graphs to long-context inputs. Instantiated with LRP-style attribution, the framework is evaluated on IFEval, GAIA2, MATH, and EvalPlus across multiple model sizes and post-training checkpoints. Along the four axes of coverage, visibility, reliability, and dynamics, contrastive attribution surfaces recurring failure modes such as underweighting task-relevant tokens and overweighting distractors, demonstrating its practical value as a scalable diagnostic tool for realistic LLM failures.