Moonshot AI

Open-weight lab

Shipped the largest open-weight model of the year. Scale is no longer what separates open from closed.

2.8T parameters, MIT-style licence

Stories

What has happened

An open source project raises serving speed for a major open AI model by up to 2.8 times

· L5 Tooling and infrastructure · 2.2x to 2.8x throughput versus vLLM's own prior release · Confirmed

Engineers working on vLLM, an open source system for running AI models, said on 13 September that they raised serving throughput for Moonshot AI's Kimi K3, an open-weight model, by 2.2 to 2.8 times against vLLM's own previous release, with latency down 56 to 60 percent over the same comparison. The work was done by engineers from Red Hat, Huawei, Inferact and Nvidia and published on vLLM's engineering blog. Several authors work at companies with a commercial stake in the hardware or software involved, so this desk treats the figure as a real but self-measured result rather than an audited one. For anyone running large open models on their own hardware instead of paying a lab by the token, a gain like this means more users served on the same machines.

Source

Cognition's new coding model, built on Moonshot's Kimi K3, claims an 81 percent cost cut

· L6 Applications and agents · 81% lower cost, 58% fewer turns · Reported

Cognition, the company behind the AI coding agent Devin, released a new model called SWE-2, built by taking Moonshot AI's 2.8 trillion parameter Kimi K3 model and further training it with reinforcement learning, a method that rewards a model for getting a real task right rather than just predicting text. Cognition says SWE-2 matches or beats its predecessor's coding performance while taking 58 percent fewer back and forth turns and costing about 81 percent less to run per task on average, and that this is the first time reinforcement learning has been applied at what the company calls a multi-trillion-parameter regime. These are Cognition's own benchmark results on its own tests, not independently verified by an outside lab. If the cost and speed gains hold up in practice, this is the kind of improvement that could make AI coding tools cheaper for the software teams and freelance developers who pay for them by the task.

Source

Three US security agencies name six Chinese AI companies, saying they pulled "billions of tokens" out of American frontier models

· Band Rules and society · 6 companies named · Confirmed

The US National Security Agency, the Cybersecurity and Infrastructure Security Agency and the FBI published a joint advisory that names DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z.AI. The agencies say these companies "extracted billions of tokens across millions of exchanges/requests" from American models including variants of Claude, GPT, Gemini and Grok, "since at least late 2024", and that this happened "likely with Chinese government awareness". Distillation, in plain terms, is training a new model on the answers of an existing one; the advisory says the technique is legitimate in research but that these campaigns were "industrial-scale". China's Ministry of Commerce replied the next day that the allegation has "no basis in fact and no basis in law" and that China "will resolutely take measures to counter" any US action against its AI companies. If US labs follow the advisory's recommendations, anyone using their models through an API may see closer monitoring of new accounts' usage and of usage patterns, both of which the advisory lists by name.

Source

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