Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality

An experiment compares a Retrieval-Augmented Generation (RAG) pipeline against Kimi K3's one-million-token context window for question answering. Using a corpus of 32 articles totaling over 127,000 tokens, the study evaluates both methods on cost, latency, and answer quality. Twelve questions of varying difficulty—single-fact, cross-article, and corpus-wide—were posed to both the RAG setup and a long-context prompt. The methodology ensured a controlled comparison by using the same model, system prompt, and a blind grading process for the generated answers.