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

A controlled comparison of a top-5 RAG pipeline and a full 127,000 token prompt on the same 12 questions, same system prompt and same model. Graded blind on correctness, completeness and grounding.

The post Kimi K3’s 1M Token Context Window vs. RAG: Cost, Latency and Answer Quality appeared first on Towards Data Science.

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How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs

Google's Open Knowledge Format (OKF) is a Markdown+YAML skeleton for sharing knowledge between humans and AI agents. This post reuses that skeleton for a very specific job β€” an agent-to-agent hand-off of pre-tokenized integer arrays between three Qwen2.5-Coder models (7B, 3B, 1.5B) β€” and shows the 28–37% TTFT reduction plus the one full-vocabulary equivalence check that keeps the whole thing safe.

The post How to Utilize OKF Efficiently to Enable Knowledge Exchange Among LLMs appeared first on Towards Data Science.

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How a Frontier Model Gets Built, Read from the Kimi K3Β Report

An open, 2.8-trillion-parameter model shipped with 47 pages of its own recipe. Reading it tells you what building a frontier model now involves, and how little of it is theΒ model.

The post How a Frontier Model Gets Built, Read from the Kimi K3Β Report appeared first on Towards Data Science.

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