MiniMax Mavis vs. MiniMax M2.5

MiniMax Mavis is a next-generation AI agent platform that transforms traditional AI assistance into a collaborative multi-agent work environment. Developed by MiniMax as an upgraded version of its original Agent product, Mavis was created to address the challenges associated with long-running, complex, and multi-step tasks that often exceed the capabilities of a single AI agent. The platform introduces Agent Teams, a framework that enables multiple AI agents with specialized roles to work together, communicate, and coordinate on projects ranging from software development and technical research to business analysis and content creation. Mavis functions as an intelligent AI butler, capable of managing workflows, tracking progress, assigning responsibilities, and executing tasks over extended periods. The system leverages MiniMax’s advanced AI models and long-context processing capabilities to maintain continuity across complex projects while minimizing context loss and task interruptions. Agent Teams can divide work among dedicated roles, allowing different agents to focus on planning, execution, verification, and quality assurance simultaneously. This collaborative approach improves efficiency, reliability, and task completion rates compared to traditional single-agent systems. Mavis is designed to support professional users who need assistance with coding projects, research assignments, document generation, operational workflows, and large-scale knowledge tasks. The platform also integrates with MiniMax’s broader ecosystem, including APIs, coding tools, and multimodal AI models, creating a unified environment for intelligent work automation. By combining multi-agent collaboration, long-term task management, and advanced reasoning capabilities, MiniMax Mavis helps organizations and individuals automate sophisticated workflows while maintaining high levels of accuracy and productivity.

MiniMax M2.5 is an advanced frontier model designed to deliver real-world productivity across coding, search, agentic tool use, and high-value office tasks. Built on large-scale reinforcement learning across hundreds of thousands of structured environments, it achieves state-of-the-art results on benchmarks such as SWE-Bench Verified, Multi-SWE-Bench, and BrowseComp. The model demonstrates architect-level planning capabilities, decomposing system requirements before generating full-stack code across more than ten programming languages including Go, Python, Rust, TypeScript, and Java. It supports complex development lifecycles, from initial system design and environment setup to iterative feature development and comprehensive code review. With native serving speeds of up to 100 tokens per second, M2.5 significantly reduces task completion time compared to prior versions. Reinforcement learning enhancements improve token efficiency and reduce redundant reasoning rounds, making agentic workflows faster and more precise. The model is available in both M2.5 and M2.5-Lightning variants, offering identical intelligence with different throughput configurations. Its pricing structure dramatically undercuts other frontier models, enabling continuous deployment at a fraction of traditional costs. M2.5 is fully integrated into MiniMax Agent, where standardized Office Skills allow it to generate formatted Word documents, financial models in Excel, and presentation-ready PowerPoint decks. Users can also create reusable domain-specific “Experts” that combine industry frameworks with Office Skills for structured, professional outputs. Internally, MiniMax reports that M2.5 autonomously completes a significant portion of operational tasks, including a majority of newly committed code. By pairing scalable reinforcement learning, high-speed inference, and ultra-low cost, MiniMax M2.5 positions itself as a production-ready engine for complex agent-driven applications.