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Hailuo 02 challenges Google Veo 3's dominance with superior performance and lower costs, thanks to its groundbreaking Noise-aware Compute Redistribution architecture that optimizes video processing efficiency.
MiniMax has unveiled the second generation of its video AI model, Hailuo 02, with significant improvements in both performance and cost efficiency. The new model leverages an innovative architecture called Noise-aware Compute Redistribution (NCR), which MiniMax claims enhances training and inference efficiency by a factor of 2.5.
Noise-aware Compute Redistribution (NCR) Architecture:
Parameter Count and Training Data:
Hailuo 02 demonstrates notable advancements in handling complex prompts and simulating physical processes. MiniMax asserts that it is currently the only model capable of accurately generating intricate scenes such as gymnastics routines.
The new model offers three variants:
These options represent a significant upgrade from the previous model, which was limited to 720p, six-second videos at 25 fps.

In the Artificial Analysis Video Arena benchmark, where users rate videos generated by competing AI models:
In user benchmarks, Hailuo 02 continues to outperform Google Veo 3, even though Veo supports native audio generation. This suggests that the quality and efficiency gains from MiniMax's NCR architecture are substantial enough to offset the lack of audio in these tests.
Since its demo launch in August last year, over 3.7 billion videos have been created using the Hailuo platform, according to MiniMax. The company attributes this rapid adoption to the model's enhanced capabilities and cost-effectiveness.
MiniMax's Hailuo 02 represents a significant leap forward in video AI technology, combining advanced architectural innovations with practical performance gains. Its ability to outperform more established models like Google Veo 3, while maintaining lower costs, makes it an attractive option for developers and content creators alike.
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About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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20 June 2025
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