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
-
位置嵌入
Absolute Position Embedding
Claude 4.5 Opus represents the high-capacity tier of the Claude 4.5 model family, engineered to manage complex reasoning and long-horizon tasks with high degrees of autonomy. The model is built upon a dense transformer architecture and utilizes a hybrid reasoning system that allows for flexible execution across varying levels of computational intensity. By integrating sophisticated tool-use capabilities and specialized computer-use functions, the model functions as a reliable orchestrator for multi-step agentic workflows and large-scale software engineering projects.
Technically, the model incorporates a 200,000-token context window and supports an architectural design that prioritizes stability in long-context retrieval and multi-file code refactoring. The underlying training methodology leverages Reinforcement Learning from AI Feedback (RLAIF) and substantial post-training to align model outputs with human-centric safety standards. Innovations such as the "effort" parameter provide developers with granular control over the model's internal deliberation process, enabling optimizations for latency or accuracy depending on the specific requirements of the production environment.
In practical application, Claude 4.5 Opus is designed for scenarios demanding rigorous analytical depth, such as financial modeling, legal analysis, and autonomous system management. Its capability to maintain state across extensive sessions makes it suitable for persistent agents that interact with external environments over hours or days. Furthermore, the model's enhanced vision and multimodal integration facilitate the processing of complex document layouts, technical diagrams, and UI-based automation tasks, ensuring consistent performance across diverse data modalities.
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.
排名
#38
| 基准 | 分数 | 排名 |
|---|---|---|
StackUnseen ProLLM Stack Unseen | 0.82 | 7 |
Graduate-Level QA GPQA | 0.87 | 7 |