1 link tagged with all of: kimi-k3 + long-context-windows + rag
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A blind test comparing RAG to Kimi K3's 1M-token context window shows long context won on answer quality but costs 16x more and runs 3x slower. The real answer isn't one or the other—it's picking the right tool based on corpus size, query volume, and task shape.
- Long context beats RAG on quality when your corpus fits under 20% of the window (tested with 127K tokens), but the cost advantage flips hard at scale: $3.82 vs $0.23 for 12 queries becomes $3,800 vs $230 for 12,000 queries on the same data.
- Query volume, not corpus size, decides the economics; corpus size decides accuracy. A small, frequently-queried corpus still favors RAG despite worse completeness.
- Three distinct tools exist for three task shapes: RAG for frequent narrow lookups on large stable corpora, long context for occasional deep reads of one corpus, and Agent Swarm/context graphs for wide research across many unconnected sources where connections matter.