Benchmarks overstate real capability
Many see MaxxBench as confirming that published scores, especially for smaller, distilled or Chinese models, collapse when tasks are slightly mutated, which matches their private testing.
Smaller models still useful with steering
Others argue the drop still leaves usable models, and that DeepSeek, GLM, Qwen and Luna do real work if tasks are chunked, well specified and iteratively guided.
Maybe it's not benchmaxxing
Some note model release dates, model versions and benchmark-detection behaviour complicate the story; mutations may trip strong priors rather than expose memorisation.