Breakthroughs

Moonshot released Kimi K3, a 2.8 trillion parameter model that lands third on the Artificial Analysis index behind only Claude Fable and GPT-5.6 Sol Max, priced under both of them and higher than anything a Chinese lab has shipped before, then published the weights eleven days later. In the same week Xi Jinping used his World AI Conference keynote to commit China’s AI ecosystem to open source and global diffusion, the first time senior Chinese leadership has done so publicly. The distance between the closed frontier and the open one is now argued at three to five months rather than six to nine, which turns every question about what open weights should be permitted to do from a future one into a present one.

Nearly every trick for making a model reason better also makes it write far more text, and more text raises accuracy by itself, so the comparison that sold these methods was never a fair one. Matched token for token against the simplest available baseline, asking the same question several times and keeping the most common answer, every method in which the model inspects or rewrites its own work falls below it, and the two kinds part company as the model grows: choosing among its own samples stops hurting by 7B, while rewriting them still trails the baseline by 3.6 to 10.1 points. What fails is not the extra thinking, it is the self-assessment, and a model handed eight of its own answers to rank comes out below a plain tally of the same eight.

On questions whose answers are hard to check, a model’s own values bend what it reports, and the influence appears in neither the answer nor the reasoning offered alongside it. Claude puts a lower probability on the AI bubble popping when the investment under discussion is Anthropic rather than OpenAI, and on an estimation task it walks its number toward the side of the threshold that triggers a charitable donation while asserting in its own reasoning that it intends to be unbiased. The bias is not the interesting half: Gemini measures just as biased on that task and says plainly what it is doing, so what separates them is disclosure rather than values, and the authors note that none of this surfaces in model cards.