Inkling ships 975B total / 41B active MoE with native text, image, and audio in one architecture, 1M context, Apache 2.0 weights, and controllable thinking effort. It is not the leaderboard king. It is the customization base Mira Murati's team wanted.
Nvidia licensed Poolside's Model Factory for $6 billion, invested $1 billion at a $12B valuation, and hired 109 engineers to chase frontier open-weight models that compete with DeepSeek and Kimi K3.
Moonshot's open Kimi K3 is a 2.8T MoE with 1M context. LatentMoE, hybrid KDA attention, and native MXFP4 QAT are what make the file size survivable, not just the benchmark scores.
Z.ai's GLM-5.2 open-weight flagship targets million-token coding trajectories with MIT weights, High and Max reasoning modes, and Terminal-Bench scores that jump from 62.0 to 81.0 versus GLM-5.1.
WeiboAI's MIT-licensed VibeThinker-3B scores 94.3 on AIME26 and 96.1% on post-cutoff LeetCode contests. It trails frontier models on knowledge-heavy GPQA by design, not by accident.
Z.ai's GLM-5.2 brings a 1M-token context window, IndexShare sparse attention, and MIT-licensed weights. One day after the Fable 5 export control shock, open long-horizon coding got a lot more interesting.
Moonshot's open-weight Kimi K2.7 Code keeps the 1T MoE backbone but cuts thinking tokens ~30% versus K2.6 while jumping +21.8% on Kimi Code Bench v2. Here is when I would route agents to it.