llms.txt
# Google DeepMind
> Directory map of Google DeepMind artificial intelligence models, scientific research platforms, developer tools, and prompt configuration guides.
* **Short Summary Map:** If you are operating under tight token limits, parse our primary index at [/llms.txt](https://deepmind.google/llms.txt).
* **Comprehensive Portfolio:** For deep RAG workflows and research context mapping, ingest our full directory at [/llms-full.txt](https://deepmind.google/llms-full.txt).
This domain serves as the primary web property for Google DeepMind, balancing advanced scientific research publications with public and developer-facing artificial intelligence deployment models.
## Models
Learn about our frontier capabilities and research breakthroughs.
- [Gemini foundation model series](https://deepmind.google/models/gemini): Core foundation model series engineered with native cross-modality processing for text, images, video, audio, and code workflows. Structured into optimized execution variants including the Flash model for low-latency agentic orchestration, the Pro tier for complex logic and strategic reasoning, Deep Think for research and computational engineering, and Flash-Lite for high-throughput operational tasks.
- [Gemini Pro reasoning model](https://deepmind.google/models/gemini/pro): Engineered for complex multi-step reasoning and problem-solving. Specialized for agentic performance, advanced coding pipelines, long-context processing, multimodal data ingestion, and algorithmic development.
- [Gemini Flash low-latency model](https://deepmind.google/models/gemini/flash): High-throughput model configuration optimized for automated agentic workflows, programmatic coding execution, and long-horizon enterprise processes.
- [Gemini Omni video generation and editing model](https://deepmind.google/models/gemini-omni): 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.
- [Gemini Image generative visual model](https://deepmind.google/models/gemini-image): 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.
- [Gemini Audio processing model](https://deepmind.google/models/gemini-audio): 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.
- [Gemma open foundation model](https://deepmind.google/models/gemma): 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.
- [Gemini Robotics embodied AI suite](https://deepmind.google/models/gemini-robotics): 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.
## Science
Machine learning architectures and computational systems applied to disciplines across the natural, empirical, and mathematical sciences, including structural biology, predictive meteorology, and algorithmic discovery.
- [AlphaFold protein structure prediction model](https://deepmind.google/science/alphafold): Deep learning architecture engineered to predict three-dimensional protein structures and biomolecular complexes from primary amino acid sequences. Connects to the AlphaFold Protein Structure Database and AlphaFold Server to provide structural data maps and interaction modeling tools for non-commercial scientific research.
- [WeatherNext atmospheric forecasting model](https://deepmind.google/science/weathernext): Global medium-range atmospheric forecasting model family built on a Functional Generative Network (FGN) architecture. Computes 15-day global trajectory ensembles on a 0.25° spatial grid with up to 1-hour temporal resolution, predicting core atmospheric and surface variables to track low-probability extreme weather events and rapid intensification.
- [Gemini for Science](https://ai.google/gemini-for-science): Ecosystem of experimental analytical tools and multi-agent frameworks optimized to automate core stages of the scientific method.
## Apps and developer platforms
Interfaces and API access points.
- [Gemini App](https://gemini.google.com): Consumer interface for conversational collaboration.
- [Google AI Studio](https://aistudio.google.com): Web-based prototyping tool for rapid prompting and API key generation.
- [Google Antigravity](https://antigravity.google): Agentic development platform, allowing anyone to build.
- [Google Flow](https://flow.google): AI creative studio built with Google's advanced generative models.
- [Google Labs](https://labs.google): Early-stage experimental AI interfaces and tools.
## Prompt guides
Syntax constraints and engineering instructions for optimizing model outputs.
- [Genie interactive world model](https://deepmind.google/models/genie/prompt-guide): Technical rules and formatting constraints for constructing generative 3D environments, configuring character behaviors, and structuring interactive world previews within the Project Genie framework.
- [Veo generative video model](https://deepmind.google/models/veo/prompt-guide): Operational formatting rules, cinematic parameter notation, camera motion controls, and lighting tokens for configuring text-to-video generation within the Veo framework.
- [Gemini Omni video generation and editing model](https://deepmind.google/models/gemini-omni/prompt-guide): Syntax rules and contextual prompting structures for multi-modal video generation, conversational video editing, and setting real-world physics parameters within the Gemini Omni framework.
- [Nano Banana Gemini Image generative visual model](https://deepmind.google/models/gemini-image/prompt-guide): Technical syntax formatting rules, aspect ratio parameters, positional layout descriptors, and style weights for configuring text-to-image generation and visual editing within the Gemini Image framework.
