DeepSeek has released its V4.1 Flash model, claiming it outperforms its previous flagship while cutting inference costs and boosting speeds – the latest salvo in China’s aggressive price-and-performance war. The Chinese artificial intelligence developer said on Thursday that V4.1 Flash used a new “Causal-Encoder-Decoder” architecture. While built on a massive 552 billion-parameter framework, it relies on a Mixture-of-Experts (MoE) design. In traditional AI, every query runs through the entire system. An MoE system routes tasks only to the specific subnetworks best suited for the job. By activating just 8 billion parameters to process inputs and 16 billion to generate responses, DeepSeek said it significantly reduced the computing power needed per request.

Full report : DeepSeek debuts DeepSeek-V4.1-Flash, its smallest model built on its new Causal Encoder-Decoder architecture, with 552 billion backbone parameters and 1 million-token context.