MiniMax-M2.7
MiniMax-M2.7 offers the same top-tier intelligence as the standard version—including recursive self-evolution and expert-level office productivity—but is designed for applications requiring sub-second latency and high-speed token generation. Leveraging an enhanced inference backbone architecture, its output speed is 66% faster than the standard model (reaching 100 tps). It is the preferred choice for interactive programming assistants, real-time agent loop execution, and high-throughput enterprise pipelines with stringent completion time requirements.
MiniMax M2.5
MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1 to extend into general office work, reaching fluency in generating and operating Word, Excel, and Powerpoint files, context switching between diverse software environments, and working across different agent and human teams.
MiniMax M2.1
MiniMax M2.1
MiniMax M2.1: Significantly Enhanced Multi-Language Programming, Built for Real-World Complex Tasks
minimax-m2
minimax-m2
minimax-m2 是一款緊湊且高效的大型語言模型，針對端到端程式設計與代理工作流程進行最佳化，擁有 10 billion 個活躍參數（230 billion 總參數），在通用推理、工具使用與多步驟任務執行方面的表現接近最先進水準，同時保持低延遲與高部署效率。該模型在程式碼產生、多檔案編輯、編譯-執行-修復迴圈，以及測試驗證中的缺陷修復方面表現突出，並在 SWE-Bench Verified、Multi-SWE-Bench、Terminal-Bench 等基準測試中取得優異成績；在 BrowseComp 和 GAIA 等代理評測中的長週期任務規劃、資訊檢索與執行錯誤復原方面亦展現出競爭力。根據 Artificial Analysis 的評級，MiniMax-M2 在數學、科學推理與指令遵循等綜合智能領域位列開源模型的頂尖行列。其較小的活躍參數量帶來快速推理、高併發與更佳的單位經濟性，非常適合大規模代理部署、開發者輔助工具，以及對回應速度與成本效率有要求的以推理為驅動的應用。