![]() |
VOOZH | about |
Parameters
-
Context Length
200K
Modality
Text
Architecture
Dense
License
Proprietary
Release Date
1 Nov 2025
Knowledge Cutoff
May 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 Opus Thinking High Effort represents the flagship intelligence tier within the Claude 4.5 model family, engineered for maximum analytical depth and extended reasoning. As a hybrid reasoning model, it incorporates an inference-time compute strategy where the model generates internal thinking blocks to deliberate on complex prompts before producing a final output. The High Effort configuration specifically adjusts the model's internal heuristics to prioritize thoroughness and multi-step verification, making it particularly effective for tasks where logical precision is more critical than immediate latency.
Architecturally, the model utilizes a dense transformer framework optimized for long-horizon task stability and coherent multi-step execution. It features a robust 200,000-token context window that supports high-fidelity retrieval and complex document analysis without significant performance degradation. The integration of an explicit 'effort' parameter allows developers to modulate the depth of the model's internal reasoning process, effectively controlling the trade-off between the number of reasoning tokens generated and the final response accuracy. This version is specifically tuned to manage sophisticated tool-use scenarios and autonomous agent workflows that require sustained focus over extended operational periods.
From a functional perspective, Claude 4.5 Opus Thinking High Effort is designed for high-stakes technical environments such as large-scale software refactoring, advanced mathematical modeling, and enterprise-grade data synthesis. It excels at interpreting ambiguous instructions and producing highly structured, executable code or detailed analytical reports. By preserving thinking blocks from previous turns within the conversational context, the model maintains a consistent logical thread across long interactions, which is essential for complex debugging and architectural design 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
#18
| Benchmark | Score | Rank |
|---|---|---|
Agentic Coding LiveBench Agentic | 0.63 | ⭐ 5 |
Coding LiveBench Coding | 0.80 | ⭐ 6 |
Mathematics LiveBench Mathematics | 0.90 | ⭐ 6 |
Reasoning LiveBench Reasoning | 0.82 | 8 |
Data Analysis LiveBench Data Analysis | 0.74 | 9 |
Overall Rank
#18
Coding Rank
#36
Total Score
C
48 / 100
©2025 ApX Machine Learning
APX AI
Online