How MiniMax’s Linear‑Attention Architecture Is Redefining Long‑Context AI Models

At the start of 2025, Chinese AI innovation surged, with DeepSeek drawing global attention and MiniMax quietly emerging as another prominent name through a series of technical breakthroughs.

Before DeepSeek’s hype, MiniMax released several multimodal models in quick succession: the video model S2V‑01 on January 10, the open‑source MiniMax‑01 model on January 15, and the high‑fidelity voice model T2A‑01‑HD on January 20. These updates demonstrate MiniMax’s comprehensive strategy in multimodal AI.

International Recognition: MiniMax Products on the a16z List

MiniMax’s product strength has also earned international market validation. Two MiniMax products—Hailuo (web application) and Talkie (mobile application)—were both featured on a16z’s global generative‑AI application rankings, highlighting the company’s competitive edge worldwide.

MiniMax‑01: Architectural Innovation Leads a New Era of Long‑Text Processing

The open‑source MiniMax‑01 series was released four days before DeepSeek R1 and immediately sparked discussion in overseas technical communities. Its breakthrough architecture and powerful long‑text handling capability have led analysts to view it as a top‑tier open‑source model that can contend with OpenAI’s offerings.

MiniMax‑01’s key innovation is its linear‑attention mechanism, the world’s first large‑scale model to achieve linear attention. This breaks the quadratic complexity bottleneck of traditional Transformers, enabling efficient processing of inputs up to 4 million tokens—32× the capacity of GPT‑4o and 20× Claude‑3.5‑Sonnet—making its overall performance comparable to leading international models.

Linear attention addresses the long‑standing quadratic complexity of traditional Transformers, where computation grows with the square of token length, causing rapid increases in required compute as sequences lengthen.

While many mainstream approaches focus on sparse‑attention—sampling parts of the attention matrix and introducing loss—MiniMax argues that this is a lossy optimization. In contrast, linear attention is a lossless solution whose efficiency advantage becomes more pronounced as model scale grows.

Moreover, for long‑text processing, linear attention offers a higher ceiling than sparse attention. As models become larger, the computational advantage of linear attention becomes increasingly significant. In the upcoming era of AI agents, the ability to handle extensive context will be essential, which is why MiniMax has placed a strong bet on linear attention.

Open‑Source Empowerment: Driving Global AI Development

Choosing the open‑source path, MiniMax’s founder Yan Junjie explained in an interview that open‑sourcing forces the team to improve algorithmic innovation efficiency while also enhancing the global technology brand.

This decision reflects MiniMax’s commitment to transparency and its sense of responsibility to advance global technology. Notably, after DeepSeek’s rise, OpenAI founder Sam Altman publicly admitted that OpenAI had historically been on the “wrong side of history” regarding open‑source, underscoring growing international recognition of China’s open‑source model approach.

Alongside DeepSeek, MiniMax‑01 and Alibaba’s Qianwen series are becoming pivotal forces in global AI development. By embracing open‑source, MiniMax not only accelerates its own innovation but also contributes a “leading‑bird” effect to worldwide technological progress.

Future Outlook: Multimodal Deep‑Inference Models on the Horizon

According to the latest information, MiniMax plans to launch a deep‑inference multimodal model based on linear attention in April‑May. This model will represent a significant breakthrough in AI.

The upcoming model boasts two major highlights: first, it will be a true multimodal deep‑inference model that deeply fuses text and visual‑language (VL) modalities, achieving a balanced blend of textual and visual understanding; second, its linear‑attention‑based deep‑inference capability will further enhance performance, building on MiniMax’s demonstrated advantages in linear‑attention experiments.

China’s New AI Card: Balancing Technological Innovation and Openness

In the increasingly competitive global AI landscape, MiniMax’s dedication to solid technical foundations and continuous innovation stands out as especially valuable.

Today, MiniMax, together with DeepSeek and Alibaba’s Qianwen series, forms a core part of China’s open‑source model ecosystem, showcasing the vibrant vitality of Chinese AI technology. As MiniMax continues to push the boundaries of linear attention, it is poised to play an increasingly important role in the global AI competition.