趋近智
参数
-
上下文长度
200K
模态
Text
架构
Dense
许可证
Proprietary
发布日期
1 Nov 2025
训练数据截止日期
May 2025
注意力结构
Multi-Head Attention
隐藏维度大小
-
层数
-
注意力头
-
键值头
-
激活函数
-
归一化
RMS Normalization
位置嵌入
Absolute Position Embedding
Claude 4.5 Opus Thinking represents a sophisticated iteration of Anthropic's flagship large language model, specifically engineered for high-stakes reasoning, autonomous agent orchestration, and complex software engineering. This model integrates a hybrid reasoning engine that allows practitioners to balance computational depth with latency through an 'effort' parameter. By enabling extended thinking mode, the model engages in a systematic internal deliberation process before producing a final response, which is particularly effective for multi-phase planning, architectural refactoring, and navigating ambiguous technical specifications.
Technically, the model is built upon a dense transformer architecture optimized for sustained coherence across long horizons. It introduces significant advancements in state management, including a dedicated memory tool that allows for persistent context across disparate sessions and advanced tool-discovery mechanisms for large-scale API environments. These architectural refinements allow the system to maintain focus on long-horizon objectives, effectively reducing reasoning drift and ensuring consistency during extended collaborative workflows.
Operationally, the model is designed for enterprise-grade applications where accuracy and structural integrity are the primary requirements. It excels in cross-file codebase analysis, financial modeling involving heterogeneous data sources, and complex browser-based automation. With a substantial context window and an improved token efficiency profile, it serves as a robust foundation for building agentic systems that require minimal human intervention and a high degree of transparency in their decision-making processes.
Enhanced Claude models with further improvements in reasoning, coding, and agentic capabilities. Features advanced thinking modes with adjustable effort levels (high, medium, standard) for optimal performance-latency tradeoffs. Excels at complex analysis, software development, web development, and long-context understanding. Includes thinking variants that expose reasoning process for improved transparency.
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