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Across six open-source LLMs (3.8-12B), four real-data regimes, 64 WIPO assistive-technology labels, two generation strategies, and three classifier families, the headline BERT-for-Patents micro-F1 jump from 0.120 to 0.702 is largely volume-driven. A duplicate-to-match real-only control that resamples 165 patents to the augmented size reaches 0.678; the controlled synthetic gain is only +0.024 over this control, but +0.219 over focal-loss reweighting, the strongest non-augmentation baseline. The main finding is that fidelity metrics change meaning with scale: at extreme scarcity, MMD correlates positively with classification gain (r=+0.95), but at 1:10 the relation flips (r=-0.73; Fisher z=+6.47, p<0.001). Fixed-budget mixing finds a 20-30% re","title":"When Does Synthetic Patent Data Help? 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