HONG KONG: Chinese AI startup DeepSeek is wrapping up its latest funding round, having generated about 100 billion yuan ($14.98 billion) from existing and new investors, four people with knowledge of the matter said on Thursday.

Battery maker Contemporary Amperex Technology (CATL) and WeChat operator Tencent Holdings were among the biggest investors in the round, said two of the people, who all declined to be named as the information was confidential.

Home appliances maker Midea, food delivery giant Meituan, automaker Geely and semiconductor maker GigaDevice became new DeepSeek investors in this round, the people said.

Other investors include Boyu Capital, CPE and Legend Capital, as well as existing shareholder IDG Capital, three of the sources said.

The total number of investors in the funding round exceeded 15, one of the sources said.

The fundraising is set to be complete in the coming days, but financial details and the investor lineup are not ironclad and could still change, a second person said.

The fundraising values DeepSeek at 500 billion yuan before new capital is injected, Reuters has reported.

DeepSeek, CATL and Tencent could not be immediately reached for comment. The other firms mentioned in this article did not immediately respond to requests for comment.

On Thursday, Bloomberg first reported, citing sources, that DeepSeek had raised $15 billion in the latest funding round, with CATL and Tencent as its main backers.

DeepSeek will have doubled its original fundraising target of 50 billion yuan, underscoring strong interest from investors - many backed by the state and big corporations - as China aims to get ahead in the AI race against the US.

The fundraising also signals that DeepSeek is getting closer to tapping public markets. In September, Reuters reported that the company had hired CITIC Securities to prepare for a ⁠potential STAR ​Market initial public offering.

Last month, the company released its latest ​model, ⁠DeepSeek-V4.1-Flash, which it said was designed for greater capability, faster inference, higher throughput and scaling to larger models. - Reuters