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BenchmarkMD-2026-0199

Quantisation costs more on reasoning than on recall

Four-bit weights barely dent factual tasks. Multi-step problems are a different story.

7 minOpen-Source AI Models

Aggregate benchmark scores hide where quantisation hurts. Factual recall and short extraction survive four-bit quantisation with losses inside the noise band. Chained reasoning does not.

If your workload is retrieval-shaped, quantise aggressively. If it involves planning across several steps, measure the specific task before trading precision for memory.

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