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URL: https://apxml.com/models/gemini-3-pro-preview-high


Gemini 3 Pro Preview High

Parameters

-

Context Length

2.1M

Modality

Multimodal

Architecture

Dense

License

Proprietary

Release Date

8 Jan 2026

Knowledge Cutoff

Oct 2025

Technical Specifications

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

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.

About Gemini 3

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.


Other Gemini 3 Models

Evaluation Benchmarks

Rank

#16

BenchmarkScoreRank

Professional Knowledge

MMLU Pro

0.90

🥈

2

General Text

Text Arena

1493

🥉

3

Graduate-Level QA

GPQA

0.919

🥉

3

0.862

8

0.74

10

Agentic Coding

LiveBench Agentic

0.55

11

0.77

20

0.82

20

Web Development

WebDev Arena

1439

23

0.75

24

Rankings

Overall Rank

#16

Coding Rank

#20

Model Integrity

Total Score

C

50 / 100