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Parameters
-
Context Length
200K
Modality
Text
Architecture
Dense
License
Proprietary
Release Date
29 Sept 2025
Knowledge Cutoff
Jan 2025
Attention
Attention Structure
Multi-Head Attention
Attention Heads
-
Key-Value Heads
-
Attention Head Dimension
-
Position Embedding
Absolute Position Embedding
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Normalization
-
Activation Function
-
Dimensions
Hidden Dimension Size
-
Number of Layers
-
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
Claude 4.5 Sonnet is a mid-tier frontier model engineered by Anthropic to deliver a refined equilibrium between high-order reasoning and operational efficiency. Designed as a production workhorse, it is specifically optimized for complex agentic workflows, large-scale software engineering, and sophisticated computer-use tasks. The model serves as a core component for autonomous systems, supporting long-running operations with a significant emphasis on reliability and instruction-following accuracy across diverse professional domains.
The underlying architecture utilizes a dense transformer-based framework that integrates a hybrid reasoning system. This system allows for two distinct modes of execution: a standard low-latency mode for rapid interaction and an extended thinking mode that exposes the model's internal reasoning process for more difficult problem-solving. It features a substantial 200,000-token context window for general availability, with a specialized 1-million-token beta capacity for handling massive datasets, entire codebases, or extensive research documentation. The implementation of absolute position embeddings and multi-head attention ensures stable performance over these long sequences.
Technically, the model introduces advanced capabilities such as parallel tool execution, which enables agents to perform multiple actions, such as executing several shell commands simultaneously, within a single turn. It is natively integrated with the Model Context Protocol (MCP) and supports specific developer tools like checkpoints for state management and context editing for precise memory control. These features make it particularly suitable for enterprise-grade applications in finance, law, and cybersecurity, where sustained focus and deep domain knowledge are required for multi-step, high-stakes tasks.
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.
Rank
#83
| Benchmark | Score | Rank |
|---|---|---|
Coding LiveBench Coding | 0.76 | 16 |
StackUnseen ProLLM Stack Unseen | 0.694 | 16 |
Graduate-Level QA GPQA | 0.834 | 16 |
Coding Aider Coding | 0.56 | 18 |
Agentic Coding LiveBench Agentic | 0.48 | 21 |
General Text Text Arena | 1454 | 22 |
Web Development WebDev Arena | 1386 | 43 |
Data Analysis LiveBench Data Analysis | 0.47 | 46 |
Mathematics LiveBench Mathematics | 0.63 | 49 |
Reasoning LiveBench Reasoning | 0.42 | 51 |
Overall Rank
#83
Coding Rank
#47
Total Score
D
38 / 100
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