關於Nex-N2-Pro
Nex-N2 is a family of thinking models with Agentic Thinking. They adaptively decide when and how deeply to reason, unifying agent cognition across coding, search, and tool use into a single coherent paradigm. Key Claims - SOTA among open models on SWE-Verified, SWE-Pro, Terminal Bench 2.0, Tau3, WildClawBench, BFCL V4 - Top-tier in agentic coding (end-to-end dev loops), deep search (BrowserComp, Wild Search, FinSearch), and real-world productivity (GDP Val) - Adaptive Thinking: auto-adjusts reasoning depth per step, 30-50% fewer thinking tokens vs always-on, with equal or better performance - Plug-and-play with Claude Code, Cursor, OpenClaw, and agentic harnesses
Harness Nex-N2-Pro’s Agentic Thinking to automate complex workflows, from autonomous coding to deep market research.
Autonomous Software Engineering
Execute end-to-end development cycles, including debugging, testing, and deployment across complex codebases.
Use Case Example:
"Fixed a memory leak in a Rust-based data pipeline by tracing ownership issues and implementing a more efficient buffer management strategy."
Deep Research & Intelligence
Perform multi-step web searches and data synthesis to generate high-fidelity reports on technical or financial topics.
Use Case Example:
"Conducted a deep-dive into global semiconductor supply chains, synthesizing data from trade journals and earnings calls to predict 2026 price volatility."
Intelligent Tool Orchestration
Seamlessly integrate with APIs and external tools to automate multi-stage business processes and administrative tasks.
Use Case Example:
"Managed a cross-platform marketing campaign by coordinating between Trello, Slack, and Google Analytics to adjust ad spend based on real-time conversion data."
Adaptive DevOps & SRE
Navigate terminal environments and cloud infrastructure to diagnose issues and optimize system performance autonomously.
Use Case Example:
"Resolved a database latency spike in a PostgreSQL cluster by analyzing query plans and reconfiguring indexing strategies via the CLI."
元數據
規格
狀態
Deprecated
架構
Transformer MoE
經過校準的
否
專家並行
是
總參數
397B
啟用的參數
推理
否
精度
FP8
上下文長度
262K
最大輸出長度

