All about Gemini 3, Google’s most intelligent model yet

Gemini 3 is Google’s latest and most advanced family of large language models (LLMs), developed by Google DeepMind. It represents a significant leap in AI capabilities, focusing on enhanced reasoning, native multimodal processing, and agentic behaviors—meaning it can autonomously plan, execute tasks, and interact with tools like a digital agent. Unlike previous models, Gemini 3 is designed to handle complex, real-world workflows, such as coding entire applications from vague prompts or generating interactive user interfaces on the fly. It’s positioned as a “phase transition” in AI, blending deep intelligence with practical utility for developers, creators, and everyday users.

At its core, Gemini 3 builds on the multimodal foundation of earlier Gemini versions (like 1.0 and 2.0) but scales up reasoning depth and reliability. It’s not just smarter; it’s more reliable in long-horizon tasks, reducing hallucinations and improving factual accuracy through better planning and tool integration.

Gemini 3 was officially released on November 18, 2025. The rollout began immediately for select users, with broader access expanding rapidly.

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• Variants:
  • Gemini 3 Pro: The flagship model, available now to all Gemini app users (with higher limits for Google AI Plus, Pro, and Ultra subscribers). It’s integrated into Google Search’s AI Mode for enhanced query handling.
  • Gemini 3 Deep Think: An upcoming mode (rolling out next week) for deeper reasoning on complex problems, allowing the model to “think” longer for better accuracy.
  • Nano Banana Pro (Gemini 3 Pro Image): A specialized image generation and editing variant, excelling in studio-quality visuals, text rendering, and infographics.
• Access:
  • Free Tier: Limited usage in the Gemini app and Google Search.
  • Paid Plans: Higher quotas via Google AI Plus/Pro/Ultra subscriptions.
  • Developer Tools: Available in Google AI Studio and Vertex AI for building apps. API pricing starts at $2 per million input tokens and $12 per million output tokens (under 200K tokens context); doubles for longer contexts.
  • Platforms: Gemini app (iOS/Android), web via gemini.google.com, and embedded in Google products like Search and Workspace.

For developers, it’s accessible via the Gemini API with new parameters for controlling latency, cost, and multimodal fidelity.

Read About: How the Gemini real-time translations work in Google Meet

Gemini 3 Key Features

Gemini 3 shines in “agentic” and multimodal tasks, making it feel like a collaborative partner rather than a simple chatbot. Here’s a breakdown:

• Multimodal Understanding: Natively processes text, images, audio, and video in a single workflow—no need for separate models. For example, it can analyze a video of a physics experiment, generate a diagram, and write explanatory code.
• Agentic Workflows: Supports autonomous coding (“vibe coding”), multi-agent collaboration (e.g., via “Antigravity” for team-based development), and tool-calling for real-time actions like web searches or API integrations.
• Generative UI: Creates entire interactive interfaces from prompts, dynamically adapting to user needs (e.g., building a custom dashboard from a sketch).
• Enhanced Reasoning: “Deep Think” mode allows extended computation for PhD-level problem-solving in math, science, and planning. It also reduces “flattery” in responses for more straightforward, reliable outputs.
• Creative Tools: Nano Banana Pro enables precise image editing with controls for lighting, aspect ratios (1:1 to 9:16), and resolutions up to 4K. It integrates real-time Google Search knowledge for accurate visuals, like recipe infographics or physics diagrams.
• Multilingual and Accessibility: Supports 140+ languages, with improved text rendering in diverse fonts and styles. Offline capabilities are expanding via related open models like Gemma 3n.

Technical Details

Gemini 3 is engineered for efficiency and power, leveraging cutting-edge architecture to balance scale with usability.

• Architecture: Built on a sparse Mixture of Experts (MoE) Transformer, which activates only relevant “experts” (sub-networks) for a query, reducing compute needs while maintaining performance. This enables native multimodal fusion—processing all input types (text, images, audio, video) in a unified pipeline from pre-training onward, unlike bolted-on systems in competitors.
• Parameters and Scale: Exact parameter counts aren’t public, but it’s estimated in the hundreds of billions (comparable to GPT-4 scale), with MoE making it 180x more cost-efficient than predecessors. The model uses Per-Layer Embedding caching and MatFormer (Mixture of Token Formers) for faster inference.
• Training Data: Trained on a vast, diverse dataset including text, code, images, and video up to January 2025 (knowledge cutoff). Emphasis on high-quality, multilingual sources (140+ languages) and real-world agentic simulations. Pre-training integrated modalities holistically, followed by fine-tuning for reasoning and safety.
• Context Window: Up to 1 million tokens, enabling “long-horizon” tasks like simulating multi-step scenarios over hours of content. (Shorter variants like Gemma 3n cap at 128K for edge devices.)
• Inference Optimizations: Supports quantized versions for on-device use (e.g., 2-3GB RAM on mobiles). New API parameters like “thinking level” let developers trade latency for depth.
• Safety and Ethics: Includes robust safeguards against biases, with evaluations for agentic risks (e.g., unintended tool misuse). Google emphasizes “helpful, honest, and harmless” alignment.

For edge deployment, the related Gemma 3n variant (open-source) runs offline on low-RAM devices, supporting multimodal inputs with privacy-focused processing.

Benchmarks and Performance

Gemini 3 dominates leaderboards, particularly in reasoning and multimodal tasks, often outperforming GPT-4o and Claude 3.5 Sonnet by 10-50% in agentic scenarios. It shows “modest leads” on trivia benchmarks but excels in compositional, time-intensive evaluations.

These gains stem from better planning (e.g., 50% improvement over Gemini 2.5 in developer tools) and reduced errors in long-context recall. In real-world tests, it handles “vibe coding” (creative, iterative development) with fewer iterations than rivals.

Comparisons to Competitors

• vs. GPT-4o (OpenAI): Gemini 3 edges out in multimodal (native video/audio) and cost (180x cheaper per token). GPT-4o is stronger in raw creative writing, but Gemini wins on agentic reliability.
• vs. Claude 3.5 Sonnet (Anthropic): Similar reasoning depth, but Gemini’s MoE makes it faster/cheaper for long tasks. Claude feels more “conversational”; Gemini is more “executive.”
• vs. Llama 3.1 (Meta): Open-source Llama is cheaper to self-host, but Gemini’s multimodal and agentic features are far ahead.

Early user feedback highlights Gemini 3’s “persistent field” feel—like a background brain integrating your data over time—making it uniquely suited for personal or team knowledge management.

Use Cases and Real-World Impact

• Developers: Build one-prompt apps, debug across repos, or simulate UIs.
• Creatives: Generate/edit production-ready images/videos with physics-accurate details (e.g., tokamak plasma flows).
• Business: Analyze docs + videos for insights, automate planning with multi-agent swarms.
• Everyday: In Search, it powers “AI Mode” for exploratory learning, like visualizing recipes or debating historical what-ifs.

Demos show it coding visualizations, writing fusion physics poems, or creating infographics from voice notes.