DeepSeek's annualized revenue exceeds $1 billion, targeting a $5 billion Series B round by end of October.

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On-chain data shows DeepSeek’s annualized revenue has reached approximately $1 billion, doubling within a few months. This growth follows API price increases and strong customer demand. The company is pursuing a Series B round targeting 5 billion yuan, with a $50 billion valuation, aiming to close by October. Ecosystem expansion is a key focus as DeepSeek prepares for an IPO on the STAR Market.

ME News reports that on September 24 (UTC+8), Beating AI broke the news that, according to two directly informed sources cited by The Information, DeepSeek’s annualized revenue has reached approximately $1 billion. Just a few months ago, this figure was under $500 million—effectively doubling in a short period. Revenue primarily comes from model APIs; the free chat app currently generates no advertising or subscription income. This growth is partly driven by price increases. DeepSeek significantly raised prices for certain APIs, with increases ranging from 2.3x to 4.5x depending on the model and time period. Liang Wenhong recently told investors that customer volume has not declined following the price hikes, and demand remains strong. Previous financial disclosures showed that DeepSeek’s API business achieved an 82.9% gross margin in the first seven months of this year. DeepSeek is also advancing its second funding round, aiming to raise approximately RMB 50 billion, with a valuation of around RMB 500 billion. This plan had been previously disclosed; the latest update indicates the company hopes to complete the funding by the end of October. DeepSeek is also preparing for an IPO on the STAR Market of the Shanghai Stock Exchange. However, the company continues to allocate the majority of its computing power to training. Liang Wenhong stated that over 70% of computing resources are dedicated to training new models, while less than 30% are allocated to inference. DeepSeek is exploring ways to run smaller models directly on gaming GPUs, reserving high-end chips exclusively for training purposes. (Source: BlockBeats)

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