DeepSeek V4 is set to officially launch in April 2026 — and this iteration is far more than a routine version bump; it's a revolutionary architectural breakthrough. The most eye-catching advance is the substantial progress in its Long-Term Memory (LTM) technology, marking a major leap for large models from "use-it-and-forget-it" to continuous learning.
Through its self-developed Engram (memory-imprint conditional memory) mechanism, V4 successfully decouples knowledge storage from dynamic reasoning at the architectural level, achieving near-O(1) knowledge retrieval. This means the model will thoroughly solve the traditional Transformer's "see-and-forget" pain point, permanently preserving conversation history and knowledge base information — clearing a critical obstacle for AI agent commercialization.
Beyond LTM, V4 also joins the global top tier in programming capability: internal tests show its HumanEval score exceeds 87.6%, surpassing leading international models. Even more impressively, its native multimodal unified architecture enables end-to-end semantic fusion of text, image, and video; supports 338 programming languages; and can digest hundreds of thousands of lines of cross-file code in one pass.
DeepSeek V4's release is not just a demonstration of China's AI technical strength — it also opens new possibilities for the open-source LLM ecosystem. When long-term memory becomes standard equipment, the boundary of AI applications will be redefined: from simple Q&A tools to intelligent partners capable of continuous learning and deep understanding of complex tasks. This technical breakthrough may lead the entire industry into a new era of memory + reasoning.