On Thursday July 16th, Moonshot AI released their latest flagship model Kimi K3. K3 is a 2.8T parameter MoE model which will have its weights released on July 27th. Much of this article follows as a reflection on the state of the ecosystem, under the assumption that Moonshot keeps their promise of the weights release date. This is a more extreme view of the equilibrium, and many of the results end up in a middle ground if the state of affairs is that China has similarly powerful, but closed models (i.e. K3 is never released).

The key fact is that either the open-to-closed or American-to-Chinese model performance gap has been reduced from the debated 6-9 months to something shorter, say 3-5 months.

From the release materials, it is clear that K3 is a true frontier model. It will be the closest open models have been to the frontier since DeepSeek R1. DeepSeek R1 was a different story. This was a Chinese lab being extremely quick to pivot to reasoning models and release one faster than many American companies. Kimi K3 an example of a Chinese lab executing on scaling the known areas: data, algorithms, architecture, tools, environments, etc.

Kimi K3 comes in at #2 overall on the Vals AI index, #3 overall on Artificial Analysis’s Intelligence Index (only beaten by Claude Fable and GPT-5.6 Sol Max while being cheaper), #1 overall in Frontend Code Arena, and more impressive results. Moonshot AI is going toe to toe with Anthropic and OpenAI with far, far fewer resources.

It is clearly the strongest open model ever released. It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and came away with the wrong conclusion that Chinese AI labs are only producing good models due to IP theft are in for an awakening – that Chinese companies are extremely good at building models in the same way the leading American companies are. Moonshot AI is solving many of the same problems that folks at OpenAI or Anthropic are solving. I’m confident there will be more distillation discussion, and pressure, but the evidence is now out that Chinese companies can do more than just fast following.

Meeting some of the core Kimi team on my trip to China, it was clear to me that they had incredible culture, some would say aura, and a freedom to express it – within the constraints of a GPU-limited environment. Where building models is so much of a scaling game, much of the ability to build a good model still comes down individual execution, motivation, and expression. Having visited them, this result is less surprising. Having visited many AI companies, very few have a culture that you can immediately pick up like this.

At the same time, China’s AI adoption trends started later than those in the U.S. So, while all the Chinese labs have way less compute than their counterparts in the U.S., more of it can certainly go to training. When I joked around about how much compute an average researcher at OpenAI could have – say a few thousand H100 equivalent machines – the researchers at Kimi were shocked. The org chart and approach to building the Kimi models surely reflect this, but it is difficult to tease out what this looks like without substantial proprietary information.

The state of affairs on peak model performance is roughly as follows:

* Anthropic – Claude Fable 5

* OpenAI – GPT 5.6 Sol

* Moonshot AI – Kimi K3 (open weights*)

* SpaceXAI – Grok 4.5

* Zhipu (Z.ai) – GLM 5.2 (open weights)

* Meta – Muse Spark 1.1

* DeepMind – Gemini Flash 3.5

* Alibaba – Qwen 3.7 Max (3.8 announced, also to be open-weights, when writing)

It is astonishing to see DeepMind, and some of the other American giants this low. In many ways, the X AI team deserves more credit. A visual summary from Artificial Analysis is below:

This release and other recent events have caused a major change in direction for the most likely outcomes in the balance between open and closed models. I’ll unpack them individually.

In many ways, it feels like the start of a new era. An era with much more competition, but also a much higher need for coordination, as we rollout incredibly powerful technologies around the world.

1. China’s recommits to open-source AI – showing a different read on near-term risks

Many people started following China’s AI scene relatively recently, so they can reach the conclusion that releasing models openly is their core strategy. In fact, I think most labs have a core strategy far closer to Anthropic or OpenAI – build the best intelligence possible. Having followed and engaged with the Chinese labs for years now, the best explanation for their original turn to releasing their models openly is practicality. They needed to release the models openly to get adoption, attention, and feedback (especially in the high-value, Bay Area market).

For a long time, there had been very limited policy in China explaining the role of open-source AI, and what could be the “country-level strategy.” To my knowledge, no senior leaders had commented on open-source AI publicly. This changed this week too, as Xi Jinping gave a keynote address at the World AI Conference (WAIC), and very directly committed the future of China’s AI ecosystem to open-source and global diffusion. This commitment to the status quo, the same week as the announcement of the strongest open-weight model to date, is a clear mark in the early history of modern AI.

This comes during a time period where many potential paths forward have been discussed for the Chinese AI industry – Will they stay open? Can they keep up with the American labs in scaling? Is there a growing revenue market in China? With these, the focus has been on China’s risk tolerance, the companies’ ability to monetize, and any closely related reason for a company to stop releasing their best models openly.

In tying Xi’s commitment in time to a very strong model, China has implicitly commented on its risk tolerance with respect to releasing open-weight models. For the time being, it is a read into the perceived risks of topics like strong cybersecurity capabilities (or bio-dangers) within the Chinese system.

The simplest explanation is that China’s government is definitely following potential risks from the models closely – likely with more technical scope than the US government’s vibe regulation – and would take action if it measured risk. The simple explanation is that they do not find current frontier models to have meaningful risk.

At the same time, China’s economic decision makers think having AI adoption is good, so they can make profits on the industry later – after growing distribution (as China has done for cars, solar, advanced manufacturing, and many areas in recent history).

These can seem somewhat shocking, in an American AI media landscape that has gone through months of hype and fearmongering over the Claude Mythos model. This surprise should be excellent grounding – the world does not have a unanimous agreement with the narratives about AI that we hear most in the U.S.

2. Open models as the economic Achilles heel of frontier labs

Many of the narrators guiding the discussion on AI have clear incentives to depress the perceived capabilities of the best, open AI models. Dean Ball – who is personally supportive of open models, but now works at OpenAI – had a widely commented on post with some reflections on Kimi, where he said the following on open models. It is important to understand the statement, as it focuses the role of open models in the economic side of the AI buildout. Dean says:

* Open-weight models are inherently decelerationist, and I’m continually surprised to see the so-called “accelerationists” so excited about open-weight models.

Explaining why open-weight models are a form of decelerationism is important to understanding the coming world order. He is right.

Open models are decelerationist economically for the frontier labs, which will slow the net investment and capex rollout for AI. This is due to the fact that strong open-weight AI models massively reduce the margin potential for the closed labs. This has two effects. First, the AI labs have fewer profits to re-invest into future models. Second, the market sees the terminal value of these companies as being lower, so they will kneecap future fundraising rounds. These together will slow timelines to the most transformative AI models, but I do not ...