Accurate classification in high-stakes domains requires not only correct predictions but transparent, traceable reasoning. We instantiate this need in clinical genomics and present VariantBench, a reproducible benchmark and scoring harness that evaluates both the final American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) labels and criterion-level reasoning fidelity for missense single-nucleotide variants (SNVs). Each case pairs a variant with deterministic, machine-readable evidence aligned to five commonly used criteria (PM2, PP3, PS1, BS1, BA1), enabling consistent evaluation of large language models (LLMs). Unlike prior work that reports only final labels, our framework scores the correctness and faithfulness of per-criterion justifications against numeric evidence. On a balanced 100-variant freeze, Gemini 2.5 Flash and GPT-4o outperform Claude 3 Opus on label accuracy and criterion detection, and both improve materially when the decisive PS1 cue is provided explicitly. Error analyses show models master population-frequency cues yet underuse high-impact rules unless evidence is unambiguous. VariantBench delivers a substrate to track such improvements and compare prompting, calibration, and aggregation strategies in genomics and other rule-governed, safety-critical settings.