- Developers & Coders: Build agentic apps with vibe coding and multi-step tool execution.
- Content Creators: Generate visuals, edit images conversationally, and create interactive experiences.
- Researchers & Analysts: Tackle long-context reasoning on papers or complex data.
- App Builders: Prototype UIs, dashboards, and games in AI Studio quickly.
- Everyday Innovators: Turn sketches or prompts into functional tools effortlessly.
How to Use Gemini 3?
- Sign Up & Access the Platform: Log into aistudio.google.com and select Gemini 3 Pro or Flash.
- Choose Your Content Type: Input text, images, video, or code for multimodal prompts.
- Generate AI-Powered Content: Craft prompts for reasoning, coding, or agentic tasks.
- Optimize & Refine: Use follow-ups, tools, or Deep Think for deeper iterations.
- Agentic Workflows: Handles multi-step tasks like planning and execution autonomously.
- Multimodal Mastery: Processes text, images, video, audio with spatial understanding.
- Vibe Coding: Creates interactive apps from high-level ideas instantly.
- Deep Think Mode: Solves ultra-hard problems with advanced reasoning chains.
- Tool Integration: Natively uses Search, code execution, and custom functions.
- Brings ideas to life with agentic power.
- Handles massive contexts without losing track.
- Multimodal makes it versatile for real tasks.
- Free access in AI Studio for quick starts.
- Can be less chatty without prompt tweaks.
- Advanced tools like some grounding limited.
- Rate limits hit during heavy experimentation.
- Preview status means occasional tweaks.
Free
Free
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Gemma
Gemma is a family of lightweight, state-of-the-art open models from Google DeepMind, built using the same research and technology that powers the Gemini models. Available in sizes from 270M to 27B parameters, they support multimodal understanding with text, image, video, and audio inputs while generating text outputs, alongside strong multilingual capabilities across over 140 languages. Specialized variants like CodeGemma for coding, PaliGemma for vision-language tasks, ShieldGemma for safety classification, MedGemma for medical imaging and text, and mobile-optimized Gemma 3n enable developers to create efficient AI apps that run on devices from phones to servers. These models excel in tasks like summarization, question answering, reasoning, code generation, and translation, with tools for fine-tuning and deployment.
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Agent Development Kit (ADK) is a flexible, open-source framework from Google for developing and deploying AI agents, optimized for Gemini and the Google ecosystem but model-agnostic and compatible with other frameworks. It treats agent development like software engineering with modular components for single or multi-agent systems, supporting hierarchical coordination, LLM-driven delegation, and rich tools including pre-built like search and code execution, custom functions, third-party integrations, and agents as tools. Developers benefit from workflow orchestration via Sequential, Parallel, Loop agents or dynamic LlmAgent transfer, built-in evaluation of responses and trajectories, native multimodal streaming for text/audio, CLI and Developer UI for debugging/visualization, containerized deployment to Vertex AI Agent Engine, Cloud Run, or Docker, and security patterns for safe agents. Available in Python, TypeScript, Go, Java with quick starts.
Agent Development Kit (ADK) is a flexible, open-source framework from Google for developing and deploying AI agents, optimized for Gemini and the Google ecosystem but model-agnostic and compatible with other frameworks. It treats agent development like software engineering with modular components for single or multi-agent systems, supporting hierarchical coordination, LLM-driven delegation, and rich tools including pre-built like search and code execution, custom functions, third-party integrations, and agents as tools. Developers benefit from workflow orchestration via Sequential, Parallel, Loop agents or dynamic LlmAgent transfer, built-in evaluation of responses and trajectories, native multimodal streaming for text/audio, CLI and Developer UI for debugging/visualization, containerized deployment to Vertex AI Agent Engine, Cloud Run, or Docker, and security patterns for safe agents. Available in Python, TypeScript, Go, Java with quick starts.
Gemini File Search
Gemini File Search is a fully managed Retrieval Augmented Generation (RAG) tool in the Gemini API that simplifies grounding AI responses in your private data by automatically handling file upload, chunking, embedding with Gemini models, indexing, and semantic retrieval for accurate, context-aware answers. Developers create persistent File Search stores to hold embeddings indefinitely, upload files directly or via Files API supporting formats like PDF, DOCX, TXT, JSON, code files, and more up to 100MB each, with custom chunking configs for optimal retrieval. It provides citations linking responses to source chunks for verification, metadata filtering for targeted searches, structured outputs for JSON schemas, and integrates seamlessly into generate Content calls without other tools. Free storage and query-time embeddings make it cost-effective, charging only initial indexing ($0.15/1M tokens) plus input/output tokens, with limits scaling by tier up to 1TB for enterprises.
Gemini File Search
Gemini File Search is a fully managed Retrieval Augmented Generation (RAG) tool in the Gemini API that simplifies grounding AI responses in your private data by automatically handling file upload, chunking, embedding with Gemini models, indexing, and semantic retrieval for accurate, context-aware answers. Developers create persistent File Search stores to hold embeddings indefinitely, upload files directly or via Files API supporting formats like PDF, DOCX, TXT, JSON, code files, and more up to 100MB each, with custom chunking configs for optimal retrieval. It provides citations linking responses to source chunks for verification, metadata filtering for targeted searches, structured outputs for JSON schemas, and integrates seamlessly into generate Content calls without other tools. Free storage and query-time embeddings make it cost-effective, charging only initial indexing ($0.15/1M tokens) plus input/output tokens, with limits scaling by tier up to 1TB for enterprises.
Gemini File Search
Gemini File Search is a fully managed Retrieval Augmented Generation (RAG) tool in the Gemini API that simplifies grounding AI responses in your private data by automatically handling file upload, chunking, embedding with Gemini models, indexing, and semantic retrieval for accurate, context-aware answers. Developers create persistent File Search stores to hold embeddings indefinitely, upload files directly or via Files API supporting formats like PDF, DOCX, TXT, JSON, code files, and more up to 100MB each, with custom chunking configs for optimal retrieval. It provides citations linking responses to source chunks for verification, metadata filtering for targeted searches, structured outputs for JSON schemas, and integrates seamlessly into generate Content calls without other tools. Free storage and query-time embeddings make it cost-effective, charging only initial indexing ($0.15/1M tokens) plus input/output tokens, with limits scaling by tier up to 1TB for enterprises.
Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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