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VOOZH | about |
Parameters
-
Context Length
200K
Modality
Text
Architecture
Dense
License
Proprietary
Release Date
1 Oct 2025
Knowledge Cutoff
Feb 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 Haiku 4.5 is a high-throughput, multimodal large language model designed for low-latency applications requiring near-frontier intelligence at scale. Within the Claude 4.5 model family, Haiku serves as the optimized execution engine, balancing computational efficiency with sophisticated capabilities such as agentic reasoning and autonomous computer use. It is engineered to handle complex, multi-step instructions and high-volume data streams, making it a primary choice for developers building responsive AI agents and real-time customer-facing services.
Technically, the model utilizes a dense transformer architecture and is trained with a specialized focus on context awareness. This architectural refinement allows the model to monitor its own token consumption within its 200,000-token context window, effectively mitigating agentic laziness and ensuring persistent reasoning during long-running tasks. Unlike many contemporary models that employ rotary embeddings, Claude 4.5 Haiku continues to utilize absolute position embeddings combined with multi-head attention (MHA) to maintain structural consistency and precision across its expanded context. The model supports multimodal inputs, enabling it to process and analyze visual data alongside text with significant speed.
Performance characteristics are centered on rapid inference and cost-effectiveness for production-grade workloads. A standout feature of this variant is the inclusion of extended thinking, which allows the model to allocate additional internal compute for deliberate reasoning before generating an output. This makes Haiku 4.5 particularly effective for sub-agent orchestration, where it acts as a fast executor for plans developed by larger models like Sonnet 4.5. Common use cases include automated financial monitoring, real-time code refactoring, and large-scale document processing where maintaining high quality at a reduced cost is a technical requirement.
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
#124
| Benchmark | Score | Rank |
|---|---|---|
StackUnseen ProLLM Stack Unseen | 0.476 | 25 |
Coding LiveBench Coding | 0.72 | 32 |
Agentic Coding LiveBench Agentic | 0.33 | 39 |
General Text Text Arena | 1410 | 49 |
Data Analysis LiveBench Data Analysis | 0.45 | 50 |
Mathematics LiveBench Mathematics | 0.58 | 54 |
Reasoning LiveBench Reasoning | 0.34 | 59 |
Web Development WebDev Arena | 1324 | 71 |
Overall Rank
#124
Coding Rank
#95
Total Score
F
34 / 100
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