From $300 million to $35 billion, Moonshot AI opens the door to the top global AI table with open-source models

Original Author: Sleepy

Kimi has just open-sourced the model weights for K3, and Moonshot AI's latest financing round has also been finalized.

According to an exclusive report from a reporter at the Star Market Daily, Moonshot AI has completed a Series F financing round, raising over $3.5 billion, achieving a post-money valuation of $35 billion.

The team had not originally planned to raise this much capital. The report states that due to investor subscriptions exceeding the original target by more than three times, Moonshot AI closed the Series F round early. Consequently, the Series G round, originally slated to begin in August this year, has been moved up.

Series G will serve as Moonshot AI's pre-IPO round, with its pre-money valuation already reaching $50 billion.

Just a week ago, market rumors suggested Moonshot AI would raise funds at a pre-money valuation of approximately $31.5 billion, followed by a final private financing round in August. Now, the Series F round has ultimately raised over $3.5 billion, valuing the company at $35 billion post-money, and the next round won't wait until August. Both the money and the valuation have arrived faster than planned.

Since its founding in 2023, Moonshot AI has taken just over three years to escalate its valuation from $300 million to $35 billion. The ongoing pre-IPO round has already placed its next price tag at $50 billion.

Over Ten Funding Rounds in Three Years, Valuation Soars from $300 Million to $35 Billion

Moonshot AI was founded in April 2023 by Yang Zhilin, Zhang Yutao, Zhou Xinyu, and Wu Yuxin.

About two months after its establishment, the company completed an Angel round exceeding $200 million, with a post-money valuation of around $300 million. For an AI startup whose product wasn't even officially launched, this was a remarkably large early-stage funding round.

The round that truly placed Moonshot AI at the center of the capital table was its Series A+ financing completed in February 2024.

This round raised over $1 billion, led by Alibaba, with participation from institutional and strategic investors including Sequoia Capital China, Xiaohongshu (Little Red Book), and Meituan. This pushed Moonshot AI's valuation to approximately $2.5 billion. A subsequent Series B round six months later raised over $300 million, further increasing the company's valuation to $3.3 billion.

However, after that 2024 fundraising spree, Moonshot AI's funding pace slowed down for a period.

During this year, the Chinese large model market underwent significant shifts. Major tech companies like ByteDance and Alibaba continuously slashed model prices. DeepSeek rapidly rose to prominence through open-source models and cost efficiency. Competition in general-purpose chat products gradually shifted from user acquisition focus to model capability, inference costs, and commercial revenue. Kimi initially broke out with its long-context capability, but relying on a single product feature alone made it increasingly difficult to support a higher valuation.

It wasn't until the end of 2025 that Moonshot AI completed a $500 million Series C round, achieving a post-money valuation of $4.3 billion. After this, the company's fundraising clearly accelerated.

In the first two months of 2026, Moonshot AI completed multiple funding rounds consecutively, with its valuation rapidly climbing from the previous $4.3 billion to $10 billion, and further reaching $18 billion.

In May of this year, the company completed a Series D round of approximately $2 billion, achieving a post-money valuation of $20 billion. Participants were no longer limited to internet companies and market-oriented investment institutions; China Mobile, Guozhituo (National Smart Investment), CPE Yuanfeng (CPE Capital), and several state-backed funds also began appearing on the shareholder list.

Right after the Series D round was completed, a new fundraising round was initiated in June. At that time, market rumors indicated a pre-money valuation had already reached $31.5 billion. Now, this round has concluded, raising over $3.5 billion, pushing Moonshot AI's post-money valuation to $35 billion.

Looking back at this fundraising curve, the most dramatic changes have occurred in the past six months.

At the end of 2025, Moonshot AI's valuation was $4.3 billion. Just over six months later, this figure has reached $35 billion, an increase of more than seven times. If the next round completes at a $50 billion pre-money valuation, Moonshot AI's valuation will have grown over 160 times in just over three years.

The reasoning from capital is becoming increasingly direct. Previously, investors were betting on Yang Zhilin and a technical team from Tsinghua University. Now, they are betting on whether Moonshot AI can become one of the few Chinese companies remaining at the global frontier model table.

After K3, Moonshot AI Returns to the Center Stage

The early closure of this funding round coincided precisely with the release of Kimi K3.

On July 16th, Moonshot AI launched Kimi K3. On July 27th, the company further open-sourced K3's complete model weights, technical report, and key infrastructure technologies.

K3 is a Mixture-of-Experts (MoE) model with a total of 2.8 trillion parameters, activating 104 billion parameters per inference instance. It supports a context window of 1 million tokens and possesses capabilities in vision, coding, reasoning, and long-duration tool use.

In its technical report, Moonshot AI stated that K3 achieves frontier-level performance on tasks involving long-form coding, agent functionality, knowledge, reasoning, and vision. Its overall capability is very close to that of the strongest closed-source models, Claude Fable 5 and GPT-5.6 Sol.

Leaderboards change constantly, and a model company's own benchmarks require a degree of scrutiny. K3's more significant impact lies in pushing the boundaries of what open-weight models can achieve.

For a long time, open-source models often played the role of followers. Closed-source companies trained the strongest models, and six months or a year later, the open-source community would replicate some capabilities at a lower cost.

K3 changed this timeline. A Chinese company directly open-sourced a model approaching global frontier capabilities, allowing other developers to download, modify, deploy it, or continue training their own models based on it.

This doesn't mean the model became cheap. K3's weight files exceed 1.5 TB. Even loading the complete model requires multiple high-end accelerator cards. Deploying it for production environments involves significant hardware investment. Its "openness" primarily addresses whether enterprises can own the model and avoid dependency on a single API provider, not that everyone can run it on their home computer.

Even so, open-weight models will inevitably impact the business of closed-source companies.

When a sufficiently powerful model can be downloaded, enterprises can deploy it on their own servers, keeping data internal, customizing the model, and avoiding perpetual token-price payments to a specific company. The barriers that closed-source models previously built through capability leadership will be eroded more quickly.

Therefore, after K3's release, the debate quickly moved beyond technical leaderboards into the realms of policy and commercial interests within the US AI industry.

The Debate Behind Open Source vs. Closed Source

On July 24th, companies and institutions including OpenAI, Google, Microsoft, Nvidia, AMD, Meta, and Hugging Face publicly supported open-weight models, opposing the US government's rush to restrict AI models available for download and deployment.

Anthropic and Amazon were not on the signatory list. This led to external questioning: is Anthropic using safety and national security as a pretext to curb the development of open models and block competitors for its own closed-source business?

On the very day K3's weights were released, Anthropic CEO Dario Amodei published an article addressing the controversy.

He stated that Anthropic never advocated for a complete ban on open-weight models. He acknowledged that open models without dangerous capabilities can be a public good, lowering costs and creating value for businesses, developers, and researchers.

However, Amodei's core position remained unchanged.

He continues to advocate restricting the flow of advanced chips and chip manufacturing equipment to China, cracking down on so-called "industrial-scale model distillation," and requiring mandatory safety testing before releasing sufficiently powerful models, regardless of whether they are open or closed source.

While ostensibly about safety, this debate fundamentally involves very real business interests.

Closed-source companies aim to retain control over the strongest models, charging fees via APIs and subscriptions. Open models, conversely, disperse capabilities to more companies, lowering prices and shortening the distance between newcomers and leading firms. The faster the opening, the harder it is for a handful of companies to lock away model capabilities within their servers perpetually.

K3 originates from China, is massive in scale, and its capabilities are near the frontier. For supporters of open models, K3 proves the open-source path can still produce top-tier models. For Anthropic, it signifies that the capability advantage US companies have built with enormous expenditure could dissipate more rapidly.

Consequently, Moonshot AI has acquired a form of capital value it previously lacked.

It is no longer just an AI application company with a large Chinese user base, nor is it just a foundational model startup awaiting a mature business model. Following the K3 launch, it is now being re-valued within the context of multifaceted competition: global open-source versus closed-source, US versus China, model capability versus computing power control.

The $3.5 billion fundraising and $35 billion valuation are the capital market's answer.

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