Why Benchmarks Alone Can't Tell You Which Model Is "Best"
A critical look at the limitations of leaderboard-driven model evaluation.
The leaderboard illusion
Public leaderboards compress a model's behavior across countless real-world scenarios into a single ranked score, which inevitably discards the context that actually determines whether a model is right for a given job.
Benchmark contamination and gaming
Popular benchmarks eventually leak into training data, and labs optimize toward known evaluation sets, both of which inflate scores in ways that don't necessarily translate into better real-world performance.
What to measure instead
Evaluating a model against your own task's actual data and failure modes — not a generic public leaderboard — remains the only reliable way to know if a model will work for your specific use case.