In 2026, the open-source big model ecosystem entered an unprecedented period of prosperity. Meta's Llama series, Alibaba's Qwen series, and the rapidly emerging DeepSeek in China are forming a tripartite structure. They each have their own strengths and demonstrate unique advantages in different scenarios.
Llama 4 remains a cornerstone of the international community. Meta has focused on optimizing its multimodal capabilities in the latest version, and Llama 4 can not only understand mixed content of graphics and text, but also handle video and audio inputs. Its flagship model with 405B parameters can compete with GPT-4o in multiple benchmark tests and is completely open source, attracting global developers to fine tune and customize it.
Qwen 3 performs outstandingly in Chinese scenarios and tool calls. The Alibaba Tongyi Qianwen team has deeply integrated agent capabilities into the base model, and Qwen-72B has shown excellent performance in function calling, code generation, and long text understanding. More importantly, its quantified version can run on consumer grade graphics cards, greatly reducing deployment barriers.
DeepSeek is the biggest dark horse of the year. Thanks to the innovation of the MoE (Mixed Expert) architecture, DeepSeek V4 achieved a performance level close to Llama 4 with less than one-third of its computing power. Its inference cost is as low as only 0.1 US dollars per million tokens, making it easy for small and medium-sized enterprises as well as individual developers to access top AI capabilities.
The choice of model depends on specific needs. Llama 4 is the perfect choice for pursuing multimodal and versatile capabilities; Emphasis is placed on Chinese interaction and agent development, making Qwen 3 more user-friendly; If the budget is limited but high-performance inference is required, DeepSeek's cost-effectiveness is unparalleled. The prosperity of the open source community enables every developer to find the most suitable tools for themselves.