This paper tackles the proliferation and inconsistency of LLM/MLLM benchmarks, proposing a unified approach to standardized evaluation across many tasks at once. It targets the growing problem that fragmented benchmarks make cross-model comparison unreliable. Useful context for anyone interpreting model leaderboards.