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Are Z.ai and MiniMax heading down opposite financial paths months after Hong Kong IPOs?

When two of China’s leading AI pioneers went public in Hong Kong in January, they pitched investors on a shared promise: capturing the explosive demand for artificial intelligence at home and abroad. Their first-half earnings, however, suggest that narrative could be splintering into two different trajectories. While Beijing-based Z.ai, also known as Zhipu AI, is winning over market analysts on…

Are Z.ai and MiniMax heading down opposite financial paths months after Hong Kong IPOs?

In the months following their Hong Kong IPOs, AI leaders Z.ai and MiniMax have taken divergent financial paths. Z.ai reported a staggering 400% increase in first-half revenue to 953.9 million yuan, compared to MiniMax's 283% growth to US$116.6 million. Z.ai's revenue grew even more dramatically when viewed through the lens of annual recurring revenue (ARR), reaching US$1.6 billion based on August's results, compared to MiniMax's US$800 million.

However, MiniMax's ARR was later revealed to be based on a single week's revenue from August, a figure that would double Z.ai's ARR to US$2 billion. Analysts like Tilly Zhang from Gavekal Dragonomics have criticized MiniMax's accounting methods, suggesting the company may be exaggerating its success. Model performance has also highlighted MiniMax's struggles, with its latest flagship M3 model scoring 45 on the Artificial Analysis Intelligence Index, behind Z.ai's GLM-5.3 and Moonshot AI's Kimi K3, which both scored 60.

This has led to skepticism about MiniMax's growth projections and a price target cut by HSBC and JP Morgan analysts. In contrast, Z.ai's ARR has reached US$1.6 billion, and CMB International analysts have raised their price target by 32% to HK$1,985, citing an effective balance between model consistency, raw intelligence, and task cost.

Despite their financial differences, both companies continue to pour substantial amounts of cash into model development and compute capacity, facing broader structural challenges such as a compute shortage.

Written by urgent.news from SCMP Tech's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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