On the eve of WAIC, Moonshot AI put Kimi K3 online — a 2.8-trillion-parameter MoE, 75% larger than DeepSeek V4 Pro (1.6T), with the full weights planned to be open-sourced on July 27. The highlights go beyond parameter count: K3 is the first to land Kimi Delta Attention (the hybrid linear-attention KDA) together with Attention Residuals, stacked on top of the FAST 2025 best-paper Mooncake's KV-cache-centric inference stack. On the performance side, K3 directly shreds the "open-source chasing closed-source" narrative: 1687 points on GDPval-AA v2, ranking third, behind Claude Fable 5 Max (1815) and GPT-5.6 Sol Max (1747.8); BrowseComp long-horizon retrieval 91.2, taking SOTA. The most critical demo is the 48-hour autonomous chip design: K3, without human intervention, completed a self-referential 4 mm², 100 MHz, 8700 tokens/s chip — pulling "long-horizon Agent" from leaderboard to a verifiable task. API $3 / $15 per million tokens, $0.30 for cache, OpenAI-SDK-compatible, with 1M context auto-cached; three tiers cover the 256K–1M window, with migration friction cut close to zero. After three years of open-source chasing closed-source, Kimi K3 uses 2.8T + linear attention + long context + fully open source + OpenAI compatibility to close all the "can't get to production" excuses at once. The moat is no longer parameter count, but algorithm, inference stack, and ecosystem design.