Reddit essay says LLM benchmarks are real evidence, but only for one setup
noninertialframe96 · reddit · 2026-07-24
This Reddit post argues that LLM benchmarks are neither pure science nor pure marketing: they are real evidence, but only for a specific experimental setup.
The author breaks down four ways benchmark scores can be shaped:
- which tasks are included,
- which harness and compute budget are used,
- how grading is done,
- which result the lab chooses to report.
The post cites recent eval-quality problems, including a July audit that found 34.1% of SWE-bench Pro tasks broken and FrontierMath v2 fixing errors in 42% of problems. The broader point is that if curating a few hundred eval tasks is already hard, then curating training data at scale is even harder—one reason Scale AI's reported 2025 revenue of about $2B is plausible. The conclusion: benchmark scores matter, but the best judge of a model is usually a private eval built from your own workload.
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