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VOOZH | about |
Parameters
-
Context Length
2.1M
Modality
Multimodal
Architecture
Dense
License
Proprietary
Release Date
8 Jan 2026
Knowledge Cutoff
Oct 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
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
-
Number of Layers
-
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
Gemini 3 Pro Preview High is a high-capacity multimodal model designed for enterprise integration and large-scale data processing. It functions as a stateful engine capable of handling data across text, image, audio, and video modalities within a single inference context. The system is engineered for high-throughput environments where multi-step task execution and complex logic are required. It operates within a unified transformer framework to maintain coherence across diverse input types, providing a stable foundation for data synthesis and cross-modal reasoning.
The architecture utilizes a dense transformer configuration with multi-head attention mechanisms optimized for long-sequence processing. It employs a specialized attention scaling strategy to manage the computational requirements associated with its two-million-token capacity. The model integrates absolute position embeddings to maintain sequence order across long inputs, ensuring that data dependencies are preserved during the decoding process. This structural choice supports the processing of large technical repositories or extensive documentation in a single inference pass, reducing the necessity for external memory retrieval systems.
In production environments, the model is applied to web development, autonomous agentic workflows, and mathematical modeling. Its multimodal capabilities allow for the direct ingestion and analysis of visual data alongside structured text, facilitating the creation of automated systems that interpret user interfaces or technical diagrams. By providing a high-capacity configuration, the model serves as a backend for demanding workloads that necessitate high-fidelity logic and precise language generation for large-scale data analysis and technical problem-solving.
Google's latest generation multimodal models with breakthrough performance across coding, mathematics, reasoning, and language understanding. Features ultra-large context windows, native multimodal processing, and thinking modes with minimal latency overhead. Available in Pro and Flash variants optimized for different workloads, with preview versions showing state-of-the-art results on multiple benchmarks.
Rank
#16
| Benchmark | Score | Rank |
|---|---|---|
Professional Knowledge MMLU Pro | 0.90 | 🥈 2 |
General Text Text Arena | 1493 | 🥉 3 |
Graduate-Level QA GPQA | 0.919 | 🥉 3 |
StackUnseen ProLLM Stack Unseen | 0.862 | 8 |
Data Analysis LiveBench Data Analysis | 0.74 | 10 |
Agentic Coding LiveBench Agentic | 0.55 | 11 |
Reasoning LiveBench Reasoning | 0.77 | 20 |
Mathematics LiveBench Mathematics | 0.82 | 20 |
Web Development WebDev Arena | 1439 | 23 |
Coding LiveBench Coding | 0.75 | 24 |
Overall Rank
#16
Coding Rank
#20
Total Score
C
50 / 100
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