Moonshot AI is scheduled to release the open weights for Kimi K3 today at 15:00 UTC. K3 itself is already available through Kimi and its API. Today’s release is different: developers will be able to download the model weights, self-host them, quantize or fine-tune the model, and integrate it into their own tools. Moonshot describes K3 as its first open 3T-class frontier model, focused on long-horizon coding, repository-scale context, tool use, browsing and multi-step planning. It is far too large for most normal local setups, so “open-weight” does not necessarily mean “easy to run locally.” AP recently reported that some US developers and companies are already adopting Chinese models such as Kimi, Z.ai/GLM and DeepSeek, mostly because of capability and cost. I’m curious about actual experience rather than launch benchmarks: Are you using Kimi, GLM, DeepSeek or Qwen in your real workflow? What are you using them for: coding, research, agents, translation or self-hosting? Where does K3 still fall behind Claude Code or Codex—reliability, tool use, speed, instruction following, context management or UX? submitted by /u/SwordfishGreedy1945
Originally posted by u/SwordfishGreedy1945 on r/ArtificialInteligence