Official Technical Resources: Google / DeepMind (page 8)
Official Technical Resources · Google / DeepMind: 135 entries in total; this page lists entries 106–120.
Gemini Deep Think reasoning mode
Specialized reasoning mode built on top of the Gemini Pro architecture, optimized for highly complex multi-step reasoning, mathematical proofing, algorithmic development, and advanced scientific problem-solving across physics, chemistry, and engineering workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Model Deployment Options · Model Selection and Cost
Link last checked: 2026-09-24
Gemini multimodal embedding model
Multimodal embedding model that maps text, images, video, audio, and documents into a single, unified semantic space. Features an 8,192 input token context, multilingual support for over 100 languages, and Matryoshka Representation Learning for scalable output dimensions (ranging from 128 to 3,072) to optimize downstream RAG, search, classification, and clustering workflows.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Embedding · Retrieval and Semantic Search
Link last checked: 2026-09-24
Gemini Omni video generation amd editing model
Omnimodal model engineered for high-resolution video generation from multimodal inputs across diverse visual styles. Supports real-world physics simulation, conversational video editing, and instruction execution across variable prompt complexities.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Multimodal Models · Streaming Output
Link last checked: 2026-09-24
Gemini Image aka Nano Banana generative visual model
Generative visual model optimized for high-precision image creation, image modification, and rapid rendering iterations. Specialized for embedding legible text within diagrams or posters, executing multi-language localized text rendering, and applying long-context factual knowledge to generate complex infographics and accurate historical scenes.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Embedding · Retrieval and Semantic Search
Link last checked: 2026-09-24
Gemini Audio processing model
Audio processing model family designed for streaming multi-turn audio, video, and text inputs. Optimized for synchronous voice and video interactions, continuous audio stream translation, and programmatic text-to-speech (TTS) synthesis.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Multimodal Models · Streaming Output
Link last checked: 2026-09-24
Lyria AI music generation model
Family of AI music generation models, including Lyria 3 and Lyria RealTime, designed for composing high-fidelity music tracks up to three minutes long. Supports multimodal image-to-audio prompting, multilingual vocal generation, detailed acoustic control, and integrated SynthID watermarking for secure content authentication.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: AI Guardrails · Privacy and Data Compliance
Link last checked: 2026-09-24
Veo generative video model
Generative video model series, featuring Veo 3.1, engineered to produce high-definition cinematic video with native, synchronized audio generation (including dialogue, ambient sound, and sound effects). Built with advanced physical world modeling and precise instruction adherence to ensure temporal consistency, realism, and creative control.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Multimodal Models · Streaming Output
Link last checked: 2026-09-24
Gemma open foundation model
Open foundation model series supporting multimodal processing across language, vision, and acoustic data. Configured for programmatic text generation, document summarization, interactive conversational agents, visual data extraction, speech transcription, and exploratory research in natural language processing (NLP) and vision-language frameworks.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Multimodal Models · Streaming Output
Link last checked: 2026-09-24
Genie interactive world model
Interactive world model series, featuring Genie 3, designed to generate and explore controllable, photorealistic 3D environments from simple text prompts. Supports real-time rendering at 720p (operating at 20–24 fps) with stable spatial consistency for sustained interaction, providing simulated environments to train and evaluate autonomous agents.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Prompt Engineering · Structured Outputs
Link last checked: 2026-09-24
Gemini Robotics embodied AI suite
Embodied AI model suite utilizing a dual-architecture system that pairs Vision-Language-Action (VLA) and Embodied Reasoning (ER) technologies. Includes Gemini Robotics 1.5 (VLA) for mapping visual and textual prompts directly to motor commands across diverse hardware platforms, and Gemini Robotics-ER 1.6 for physical-world planning, autonomous decision-making, and natural language coordination.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: AI Agent · Multimodal Models
Link last checked: 2026-09-24
SynthID digital watermarking technology
Watermarking technology designed to embed imperceptible, tamper-resistant digital identifiers directly into AI-generated text, images, video, and audio. It adjusts token probability scores for text generation and introduces robust, invisible markers to media files, preserving content quality while enabling verification via the SynthID Detector portal or direct integration with Gemini.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: AI Guardrails · Privacy and Data Compliance
Link last checked: 2026-09-24
DiLoCo distributed training architecture
Distributed, low-communication AI training architecture designed to train large language models across geographically distant data centers. By structuring training runs across decoupled "islands" of compute with asynchronous data flow, it isolates local hardware failures to enable continuous, self-healing training, operates efficiently over standard wide-area network bandwidth (2–5 Gbps), and supports mixing heterogeneous hardware generations in a single run without sacrificing performance.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Model Deployment Options · Model Selection and Cost
Link last checked: 2026-09-24
CodeMender automated software security agent
AI-powered agent for automated software security that identifies, patches, and proactively refactors vulnerable source code. Powered by Gemini Deep Think models, it utilizes advanced program analysis (including static, dynamic, differential testing, and SMT solvers) alongside a multi-agent critique loop to generate and validate regression-free security patches and secure API rewrites for production codebases.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: AI Agent · Code Execution and Sandboxing
Link last checked: 2026-09-24
SIMA 2 virtual environment agent
Gemini-powered AI agent designed for interactive reasoning and goal execution across diverse, virtual 3D environments. Operating via standard visual inputs and keyboard/mouse controls without direct game-engine access, it generalizes complex instructions across unseen games and newly generated world simulations (Genie 3) while demonstrating closed-loop, self-improvement capabilities via autonomous play.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: AI Agent · Multimodal Models
Link last checked: 2026-09-24
AlphaGo reinforcement learning system
Historical reinforcement learning system and the first computer program to defeat a professional human Go player and a world champion. It combined deep neural networks—utilizing policy networks for move selection and value networks for position evaluation—with Monte Carlo Tree Search (MCTS) to navigate the game's vast search space.
Evidence use: For understanding, implementation, selection, reliability, security or migration decisions.
Limitations: Directly evidences only Google's own implementation and claims. Recheck preview models, pricing and lifecycle before use.
Related concepts: Reinforcement Learning · Model Selection and Cost
Link last checked: 2026-09-24