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Description

MiniMax M3 is a frontier open-weight AI model built for coding, agentic work, multimodal understanding, and ultra-long-context tasks. The model supports up to a 1 million token context window, allowing it to work across large codebases, long documents, logs, project histories, and complex task environments. MiniMax M3 introduces MiniMax Sparse Attention, a sparse attention architecture designed to make long-context processing more efficient. The model is natively multimodal, with training that supports deeper semantic fusion across text, image, and video inputs. It is designed to support software engineering tasks, repository analysis, terminal-style work, browser-style retrieval, tool use, and autonomous workflows. MiniMax M3 has a mixture-of-experts architecture with hundreds of billions of total parameters and a smaller activated parameter count for more efficient inference. Developers can use it for AI coding assistants, workflow automation, research agents, document analysis, visual reasoning, and enterprise AI systems. Its long-context capability makes it especially useful when tasks require many files, references, instructions, or interaction histories to stay available at once. MiniMax M3 helps teams build more capable AI agents that can understand larger problems, work across multiple modalities, and execute complex tasks with stronger context awareness.

Description

Xgen-small is a compact language model crafted by Salesforce AI Research that is tailored for enterprise use, offering efficient long-context capabilities at a manageable cost. It employs a combination of focused data curation, scalable pre-training, length extension, instruction fine-tuning, and reinforcement learning to address the intricate and high-volume inference needs of contemporary businesses. In contrast to conventional large models, Xgen-small excels in processing extensive contexts, allowing it to effectively synthesize insights from various sources such as internal documents, code bases, research articles, and real-time data feeds. With parameter sizes of 4B and 9B, it strikes a careful balance between cost efficiency, privacy protections, and comprehensive long-context comprehension, positioning itself as a reliable and sustainable option for large-scale Enterprise AI implementation. This innovative approach not only enhances operational efficiency but also empowers organizations to leverage AI effectively in their strategic initiatives.