Public benchmarks measure general model capability under standardized conditions; your product needs task-specific evaluation under your conditions. The gap shows up in several ways:
- Distribution mismatch: MMLU (Massive Multitask Language Understanding) is multiple-choice academic knowledge; HumanEval is small self-contained Python functions;
SWE-benchis GitHub issue fixing. None resembles your support bot answering questions about your refund policy with your retrieval pipeline and system prompt. - Contamination: benchmark questions leak into training data, inflating scores in ways that say nothing about generalization to your inputs.
- Saturation: strong models can cluster near a benchmark's ceiling, so small score differences no longer separate them well.
- System vs model: benchmarks test the raw model; your quality depends on the whole system: prompts, retrieval, tools, guardrails, and how they interact.
- Metric mismatch: a model can top LMArena on style preference while being worse at your domain's factual accuracy.
Benchmarks are useful for a first shortlist of candidate models. The decision of which model actually ships should come from running your own eval suite against each candidate.
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