Google’s Gemini 3 launched November 17, 2025, and immediately claimed the top spot on LMArena’s leaderboard with a breakthrough 1501 Elo score beating every competing AI model including GPT-4, Claude, and its predecessor Gemini 2.5 Pro. The new model combines state-of-the-art reasoning with native multimodal understanding, offering capabilities that span from PhD-level problem-solving to autonomous coding and real-time task execution across Google’s ecosystem.
This isn’t just another incremental update Gemini 3 represents Google’s boldest move yet in the AI race, introducing an enhanced reasoning mode called Deep Think, a revolutionary agentic development platform named Antigravity, and immediate integration into Google Search through AI Mode. For developers, researchers, and tech enthusiasts wondering whether Google has finally caught up to OpenAI, the benchmarks tell a compelling story.
Table of Contents
What Makes Gemini 3 Different
State-of-the-Art Reasoning Architecture
Gemini 3 Pro’s core breakthrough lies in its reasoning depth and contextual understanding. Unlike previous models that required extensive prompting to understand complex requests, Gemini 3 automatically grasps nuance and intent with minimal input. Google’s CEO Sundar Pichai described this evolution dramatically: “In just two years, AI has evolved from simply reading text and images to reading the room”.
The model achieves 37.5% on Humanity’s Last Exam without tools, a benchmark designed to test PhD-level reasoning across multiple disciplines and scores 91.9% on GPQA Diamond, which evaluates graduate-level science questions. These scores represent significant jumps from Gemini 2.5 Pro and position Gemini 3 ahead of competing frontier models.
Multimodal Understanding at Scale
Where Gemini 3 truly separates itself is multimodal reasoning processing text, images, video, audio, and code seamlessly within a 1 million-token context window. The model scores 81% on MMMU-Pro (multimodal understanding) and 87.6% on Video-MMMU, establishing new benchmarks for visual comprehension. Practical applications include analyzing video lectures to generate interactive study materials, deciphering handwritten recipes in multiple languages, or reviewing sports footage to create personalized training plans.
Google’s engineering team built granular controls through the new media_resolution parameter, allowing developers to balance detail recognition against token usage and latency depending on use case requirements.
Benchmark Performance Breakdown
LMArena Leaderboard Dominance
Gemini 3 Pro’s 1501 Elo score on LMArena represents the highest rating achieved by any public AI model as of November 2025. For context, Gemini 2.5 Pro held the previous top position at 1451 Elo for over six months, while competing models like GPT-4 and Claude typically scored in the 1400-1480 range. The 50-point jump reflects measurable improvements across reasoning, coding, multimodal tasks, and factual accuracy.
| Benchmark | Gemini 3 Pro | Gemini 2.5 Pro | Improvement |
|---|---|---|---|
| LMArena Elo | 1501 | 1451 | +50 points |
| Humanity’s Last Exam | 37.5% | 28.2% | +9.3% |
| GPQA Diamond | 91.9% | 84.1% | +7.8% |
| MMMU-Pro (Multimodal) | 81.0% | 68.5% | +12.5% |
| MathArena Apex | 23.4% | 0.5% | +22.9% |
| SWE-bench Verified | 76.2% | 58.3% | +17.9% |
These numbers show particular strength in mathematical reasoning, coding, and multimodal understanding areas where previous Gemini versions lagged behind competitors.
PhD-Level Reasoning Scores
Academic benchmarks reveal Gemini 3’s capability for complex analytical thinking. The 37.5% score on Humanity’s Last Exam places it well above human expert performance on many subtasks, while the 91.9% on GPQA Diamond approaches near-perfect accuracy on graduate-level physics, chemistry, and biology questions. These aren’t cherry-picked marketing metrics; they represent independent evaluations conducted by AI research organizations and verified by third-party auditors including Apollo and Vaultis.
Gemini 3 also achieves 72.1% on SimpleQA Verified, a benchmark specifically designed to measure factual accuracy and reduce hallucinations, a persistent challenge for large language models.
Coding and Math Capabilities
For developers, Gemini 3 Pro’s coding performance marks a major leap forward. The model tops WebDev Arena with a 1487 Elo score and achieves 76.2% on SWE-bench Verified, which tests AI’s ability to fix real GitHub issues across multiple programming languages. It handles 54.2% of Terminal-Bench 2.0 tasks, demonstrating practical tool-use abilities for operating computers via command line.
Mathematical reasoning shows even more dramatic improvement: 23.4% on MathArena Apex compared to 0.5% for Gemini 2.5 Pro, a 46x increase on competitive math problems. Developers report that Gemini 3 requires significantly less prompt engineering to generate functional, well-structured code from natural language descriptions.
Gemini 3 Deep Think Mode
Enhanced Reasoning Explained
Gemini 3 Deep Think represents Google’s answer to extended reasoning systems, similar to OpenAI’s o1 model. Rather than rushing to a response, Deep Think mode allocates structured “thinking time” to break down complex problems, evaluate multiple approaches, and validate solutions before presenting results. This produces measurably better outcomes on tasks requiring multi-step planning, mathematical proofs, or ambiguous problem definitions.
Performance numbers demonstrate the value: Deep Think achieves 41.0% on Humanity’s Last Exam (versus 37.5% for standard Gemini 3 Pro), 93.8% on GPQA Diamond (versus 91.9%), and an unprecedented 45.1% on ARC-AGI-2 a benchmark specifically designed to test novel problem-solving and generalization.
When to Use Deep Think
Google positions Deep Think as a mode rather than a separate model, meaning developers can selectively enable it for queries where quality justifies increased latency and cost. Ideal use cases include mathematical problem-solving, multi-file code refactoring, long-document analysis requiring evidence synthesis, strategic planning with constraints, and research tasks needing citation verification.
The mode introduces new API controls including thinking level (low vs. high), thought signatures for transparency into reasoning steps, and media resolution adjustments for multimodal inputs. Teams typically start by enabling Deep Think only in workflows where correctness matters more than speed production debugging, financial analysis, or academic research rather than chatbot responses or simple CRUD operations.
Currently, Deep Think access is limited to safety testers, with broader availability to Google AI Ultra subscribers planned for late November or early December 2025.
Real-World Capabilities
Learn Anything with Multimodal Input
Gemini 3’s native multimodal architecture enables learning workflows that previous models couldn’t handle. Upload academic papers, video lectures, or tutorial content, and the model generates interactive flashcards, code-based visualizations, or practice problems tailored to your learning style. The 1 million-token context window means you can process entire textbooks or semester-long course materials in a single session.
Google demonstrates this with examples like translating handwritten family recipes across languages to create digital cookbooks, or analyzing sports performance videos to identify technique improvements and generate training plans. AI Mode in Search now uses Gemini 3 to create immersive visual layouts, interactive simulations, and generative UI experiences that adapt to individual queries.
Build Interactive Apps with Vibe Coding
“Vibe coding” refers to Gemini 3’s ability to generate fully functional applications from conversational descriptions rather than technical specifications. Developers report creating retro 3D games, interactive data visualizations, and complex web interfaces from single-sentence prompts. The model handles both creative direction and technical implementation simultaneously understanding aesthetic intent while generating clean, maintainable code.
Gemini 3 Pro excels at zero-shot generation with complex requirements, producing richer visualizations and deeper interactivity than previous versions. It’s available through Google AI Studio, Vertex AI, Gemini CLI, and third-party platforms including Cursor, GitHub Copilot, JetBrains IDEs, Replit, and Manus.
Plan Complex Tasks with Gemini Agent
Gemini Agent represents Google’s push into autonomous task execution. By combining deeper reasoning with consistent tool use, the agent handles multi-step workflows from start to finish booking services, organizing email inboxes, managing calendar conflicts, or coordinating across multiple apps. Performance on Vending-Bench 2 shows Gemini 3 maintaining consistent decision-making over simulated year-long planning horizons without task drift.
Currently, Gemini Agent is available exclusively to Google AI Ultra subscribers through the Gemini app, with expansion to additional Google products planned for early 2026. The system operates under user control, requiring confirmation before executing sensitive actions or making purchases.
Google Antigravity Platform
What Is Agentic Development
Google Antigravity flips traditional development workflows by making AI agents the primary actors rather than assistants. Instead of writing code line-by-line with Copilot suggestions, developers describe high-level goals while autonomous agents handle implementation, testing, and validation across the editor, terminal, and browser simultaneously.
The platform introduces an “Agent-first Manager” (nicknamed Mission Control) where developers spawn, direct, and observe multiple agents working asynchronously. Each agent produces verifiable artifacts, task lists, implementation plans, screenshots, browser recordings that developers review and comment on similar to Google Docs collaboration. This elevates development from tactical coding to strategic architecture.
Key Features and Workflow
Antigravity comes bundled with Gemini 3 Pro for agentic workflows, Gemini 2.5 Computer Use for browser automation, and Nano Banana (Gemini 2.5 Image) for visual editing tasks. Agents independently plan multi-step software tasks, write code across multiple files, run validation tests in Chrome, and present walkthrough demonstrations of completed work.
The platform is available as a free public preview for MacOS, Windows, and Linux, with support for alternative models from Anthropic and OpenAI if teams prefer different reasoning engines for specific tasks. Google built Antigravity around four principles: trust (verifiable outputs), autonomy (minimal micromanagement), feedback (collaborative refinement), and self-improvement (agents learning from corrections).
How to Access Gemini 3
Free Access Methods
Google offers multiple free pathways to test Gemini 3 capabilities:
Gemini App: Available immediately to all users (free tier) with Gemini 3 Pro as the default model for conversations, multimodal uploads, and Canvas interactions
AI Mode in Search: Accessible through Google Search for logged-in users, providing Gemini 3-powered AI Overviews with generative UI and interactive simulations
Google AI Studio: Free developer playground at aistudio.google.com with API access, code examples, and prompt testing for Gemini 3 Pro preview
Gemini CLI: Command-line interface for terminal-based interactions with Gemini 3 models
The free tier includes core Gemini 3 Pro capabilities but limits usage through rate limiting and monthly quotas. Gemini Agent and Deep Think mode remain exclusive to paid subscribers.
Pricing for API and Enterprise
Gemini 3 Pro preview pricing through Google AI Studio and Vertex AI operates on tiered input/output token costs:
| Usage Tier | Input (per 1M tokens) | Output (per 1M tokens) |
|---|---|---|
| < 200K tokens | $2.00 | $12.00 |
| ≥ 200K tokens | $4.00 | $18.00 |
These rates represent 20-35% increases compared to Gemini 2.5 Pro ($1.50 input / $15.00 output), reflecting enhanced capabilities and computational requirements. Google positions this as preview pricing, with general availability rates to be announced when the model exits preview status in Q1 2026.
Enterprise customers using Vertex AI get additional features including VPC controls, audit logging, SLA commitments, and priority support. Google AI Ultra subscriptions ($29.99/month) include unlimited Gemini 3 access through consumer products plus exclusive features like Gemini Agent and upcoming Deep Think mode.
Gemini 3 vs Competitors
Gemini 3 vs GPT-4 Comparison
Gemini 3 Pro outperforms GPT-4 across most standardized benchmarks as of November 2025. On MMLU (Massive Multitask Language Understanding), Gemini scores 90.0% compared to GPT-4’s ~86%, with larger gaps on coding (74.4% vs 67% on HumanEval) and mathematics (94.4% vs 92% on GSM8K with chain-of-thought prompting).
The most significant advantage appears in multimodal reasoning Gemini 3’s native architecture for processing images, video, and audio surpasses GPT-4’s vision capabilities, particularly for complex visual understanding and long-form video analysis. Gemini 3 also offers a 1 million-token context window compared to GPT-4’s 128K tokens, enabling analysis of much larger documents or codebases.
OpenAI’s o1 model (optimized for reasoning) still competes closely with Gemini 3 Deep Think on mathematical and scientific benchmarks, though direct comparisons are complicated by different evaluation methodologies and model release dates.
Gemini 3 vs Claude Performance
Anthropic’s Claude models generally emphasize safety and detailed explanations over raw benchmark performance. While Claude 3.5 Sonnet remains competitive on many tasks, Gemini 3 Pro demonstrates measurably higher scores on academic reasoning tests, coding challenges, and multimodal understanding.
Claude’s strength lies in conversational quality, nuanced writing, and reluctance to produce potentially harmful content sometimes at the expense of directness. Gemini 3’s responses are described as more concise and pragmatic, “trading cliché and flattery for genuine insight”. For use cases demanding maximum accuracy on technical problems complex math, scientific reasoning, code generation Gemini 3 and GPT-4 currently lead the field, with Claude as a strong alternative prioritizing safety.
Who Should Use Gemini 3
Developers building AI-powered applications gain access to best-in-class coding assistance, agentic workflows through Antigravity, and API integration options competitive with OpenAI and Anthropic offerings. The vibe coding capabilities make rapid prototyping significantly faster, while multimodal understanding enables novel app experiences.
Researchers and academics benefit from PhD-level reasoning on scientific questions, long-context analysis of papers and datasets, and Deep Think mode for complex problem-solving requiring extended reasoning chains. The factual accuracy improvements (72.1% on SimpleQA Verified) make Gemini 3 more reliable for research applications than previous models.
Enterprise teams evaluating AI adoption should consider Gemini 3’s integration across Google Workspace, competitive API pricing for high-volume usage, and enterprise controls through Vertex AI. Organizations already using Google Cloud infrastructure may find Gemini 3 easier to deploy than alternatives requiring new vendor relationships.
Content creators and educators can leverage multimodal learning tools, interactive content generation, and the generative UI capabilities in AI Mode for creating engaging educational materials. The 1 million-token context window enables processing entire courses or video series in single sessions.
Gemini 3 Pro Model Specifications
Architecture: Transformer-based multimodal model with native support for text, images, video, audio, and code processing in unified latent space
Context Window: 1,048,576 tokens (1 million tokens)
Training Data Cutoff: August 2025
Supported Input Formats:
- Text (all major languages with enhanced multilingual performance)
- Images (JPEG, PNG, WebP, GIF)
- Video (MP4, MOV, AVI up to 2 hours)
- Audio (MP3, WAV, FLAC)
- Code (40+ programming languages)
Output Capabilities:
- Text generation up to 128 output tokens per second
- Code generation with syntax highlighting
- Structured data (JSON, XML, YAML)
- Interactive web UI components
API Parameters:
media_resolution: Control visual processing detail (low/medium/high/ultra)thinking_level: Enable enhanced reasoning (low/high for Deep Think mode)temperature: Control randomness (0.0-2.0)top_p,top_k: Nucleus and top-k samplingmax_output_tokens: Limit response length
Safety & Security:
- Reduced sycophancy compared to previous models
- Enhanced prompt injection resistance
- Improved protection against adversarial attacks
- Evaluated by UK AISI, Apollo, Vaultis, Dreadnode
- Comprehensive safety evaluations per Google’s Frontier Safety Framework
Availability:
- Preview release as of November 17, 2025
- General availability expected Q1 2026
- Accessible through Gemini API, Vertex AI, Gemini CLI, third-party platforms
Comparison Table: Gemini 3 Pro Vs Top Competitors
| Feature | Gemini 3 Pro | GPT-4 | Claude 3.5 Sonnet | Gemini 2.5 Pro |
|---|---|---|---|---|
| LMArena Elo Score | 1501 | ~1470 | ~1460 | 1451 |
| Context Window | 1M tokens | 128K tokens | 200K tokens | 1M tokens |
| Multimodal Support | Native (text, image, video, audio, code) | Vision API | Vision API | Native |
| MMLU Benchmark | 90.0% | ~86% | ~88% | 85.2% |
| HumanEval Coding | 74.4% | 67% | 70% | 68.1% |
| SWE-bench Verified | 76.2% | ~60% | ~65% | 58.3% |
| Video Understanding | 87.6% (Video-MMMU) | Limited | Limited | 75.3% |
| Enhanced Reasoning Mode | Deep Think (41% Humanity’s Last Exam) | o1 (similar performance) | Extended Thinking (limited) | None |
| API Pricing (per 1M output tokens) | $12-18 | $15-30 | $15 | $15 |
| Free Tier Access | Yes (Gemini app, Search) | Limited (ChatGPT) | Limited (Claude.ai) | Yes |
| Enterprise Platform | Vertex AI | Azure OpenAI | Claude for Work | Vertex AI |
| Agentic Development Tool | Antigravity (native) | GitHub Copilot (third-party) | None | None |
| Release Date | November 17, 2025 | March 2023 (GPT-4), ongoing updates | June 2024 | February 2025 |
Frequently Asked Questions
What is Gemini 3 and how is it different from ChatGPT?
Gemini 3 is Google’s latest AI model, released November 17, 2025, featuring state-of-the-art reasoning and native multimodal understanding across text, images, video, audio, and code. Unlike ChatGPT (which uses GPT-4), Gemini 3 offers a 1 million-token context window, superior benchmark scores on academic reasoning and coding tasks, and tight integration with Google products including Search, Workspace, and Cloud Platform. Gemini 3 Pro currently tops the LMArena leaderboard at 1501 Elo, outperforming GPT-4’s typical 1450-1480 range.
How can I access Gemini 3 for free?
Free access is available through the Gemini app (gemini.google.com), AI Mode in Google Search, Google AI Studio developer playground, or Gemini CLI command-line tool. All methods provide core Gemini 3 Pro capabilities with rate limits and usage quotas. Advanced features like Gemini Agent and Deep Think mode require Google AI Ultra subscription ($29.99/month).
What is Gemini 3 Deep Think mode?
Deep Think is an enhanced reasoning mode that allocates additional computational resources for complex problem-solving, achieving 41.0% on Humanity’s Last Exam and 45.1% on ARC-AGI-2 benchmarks. It’s designed for tasks requiring multi-step reasoning, mathematical proofs, or ambiguous problem definitions where accuracy matters more than response speed. Deep Think will be available to Google AI Ultra subscribers in late November or early December 2025 following safety testing.
How much does Gemini 3 API cost for developers?
Preview pricing through Google AI Studio and Vertex AI charges $2 per million input tokens and $12 per million output tokens for usage under 200K tokens, increasing to $4 input / $18 output above that threshold. This represents approximately 20-35% higher costs than Gemini 2.5 Pro but includes significantly enhanced capabilities. Final general availability pricing will be announced in Q1 2026.
What is Google Antigravity?
Antigravity is an agentic development platform where AI agents autonomously plan and execute complex software tasks across the editor, terminal, and browser. Unlike traditional IDEs with AI assistants, Antigravity positions agents as primary actors, handling multi-step development workflows while producing verifiable artifacts for developer review. It’s available as a free public preview for MacOS, Windows, and Linux with Gemini 3 Pro integration.
Is Gemini 3 better than GPT-4 at coding?
Gemini 3 Pro outperforms GPT-4 on multiple coding benchmarks, scoring 76.2% on SWE-bench Verified (real GitHub issue resolution) and topping WebDev Arena with 1487 Elo. Developers report improved zero-shot code generation, better handling of complex requirements, and superior vibe coding capabilities for creating interactive web applications from natural language descriptions. For production coding tasks requiring autonomous agents, Antigravity’s integration gives Gemini 3 a distinct advantage.
When will Gemini 3 Deep Think be available to everyone?
Google is currently conducting safety evaluations with testers before releasing Deep Think to Google AI Ultra subscribers ($29.99/month) in late November or early December 2025. There’s no announced timeline for free tier access or broader availability beyond Ultra subscriptions. Standard Gemini 3 Pro without Deep Think is available immediately to all users through multiple free channels.
Can Gemini 3 analyze videos and images?
Yes, Gemini 3 achieves 87.6% on Video-MMMU benchmarks and 81% on MMMU-Pro for image understanding. Practical applications include analyzing lecture videos to generate study materials, reviewing sports footage for technique improvements, deciphering handwritten documents in multiple languages, and creating interactive visualizations from visual data. The model’s native multimodal architecture processes visual content without conversion to text descriptions, enabling more accurate understanding than competitors.
Featured Snippet Boxes
What is Gemini 3?
Gemini 3 is Google’s most advanced AI model, launched November 17, 2025, combining state-of-the-art reasoning with native multimodal understanding. It tops the LMArena leaderboard at 1501 Elo, demonstrating PhD-level performance on academic benchmarks while offering capabilities spanning coding, visual analysis, and autonomous task execution across Google’s ecosystem.
Gemini 3 Key Features
Gemini 3 delivers breakthrough performance through 1 million-token context windows, 91.9% accuracy on PhD-level science questions, 76.2% success on real GitHub coding issues, and native processing of text, images, video, and audio. Deep Think mode extends reasoning capabilities further, achieving 45.1% on novel problem-solving benchmarks.
How to Access Gemini 3
Free access available through the Gemini app, AI Mode in Google Search, Google AI Studio, or Gemini CLI. Google AI Ultra subscribers ($29.99/month) unlock Gemini Agent for autonomous tasks and upcoming Deep Think mode. Developers pay $2-4 per million input tokens through API services.
Gemini 3 vs GPT-4
Gemini 3 Pro outperforms GPT-4 on most benchmarks with 90.0% on MMLU versus ~86%, 74.4% on coding challenges versus 67%, and superior multimodal reasoning across images and video. Gemini offers a 1 million-token context window compared to GPT-4’s 128K tokens.
Google Antigravity Explained
Antigravity is Google’s agentic development platform where AI agents autonomously plan and execute software tasks across editor, terminal, and browser. Using Gemini 3 Pro, agents produce verifiable artifacts implementation plans, screenshots, browser recordings while developers operate at higher strategic levels.
Gemini 3 Benchmark Scores
Gemini 3 Pro achieves 1501 Elo on LMArena, 37.5% on Humanity’s Last Exam, 91.9% on GPQA Diamond, 23.4% on MathArena Apex, 76.2% on SWE-bench Verified, and 72.1% on SimpleQA Verified establishing new standards across reasoning, coding, and factual accuracy.
