What is Gemini 3: Not Just an Update, But a Leap in Thinking

In today’s world where artificial intelligence (AI) develops at a staggering pace, we seem to have become somewhat numb to new model releases. Every few months brings a new breakthrough, yet moments that truly make us jump from our chairs and exclaim “this is incredible” are becoming increasingly rare. However, Google’s official release of Gemini 3 on November 18, 2025, represents exactly that kind of long-awaited breakthrough.

What is Gemini 3? Simply put, it’s the latest generation multimodal AI model developed by Google DeepMind, and currently Google’s most intelligent AI model. This update feels less like a routine iteration and more like a fundamental leap in thinking. The capabilities demonstrated by Gemini 3 have transcended simple question-answering or text generation, entering entirely new territory of creation, reasoning, and even autonomous action.

According to the official Google announcement, Gemini 3 set new records across multiple benchmarks, including achieving the highest score of 37.4 on the Humanity’s Last Exam test, surpassing the previous record of 31.64 held by GPT-5 Pro. Even more impressively, Gemini 3 Deep Think mode achieved 41.0% accuracy on the same test, demonstrating unprecedented reasoning capabilities.

In this article, we’ll share five of Gemini 3’s most astonishing and impactful superpowers based on actual testing. Be prepared—your understanding of AI may be about to be transformed.

Five Mind-Blowing Capabilities of Gemini 3

First: Not Just Coding, But “One-Click Generation” of Complete Games

In the first test, testers gave Gemini 3 a single instruction: create a Minecraft-like voxel block world using only HTML, CSS, and JavaScript. Incredibly, it completed this task with one command, without using any external libraries, generating all necessary code from scratch.

The game not only ran but allowed players to move, place, and remove blocks. Next, testers challenged it to create a Vampire Survivors clone, and it succeeded on the first try. When feedback indicated the game was too fast, it adjusted based on the input and rebalanced the gameplay.

This marks AI’s evolution from a simple “syntax translator” to a development partner that understands “design intent.” This capability is called “Vibe Coding“—executing programming tasks through natural language instructions, where Gemini 3’s performance far exceeds expectations.

“This was completely done in one shot. It didn’t use any external libraries. The tester was absolutely amazed by these results.”

According to TechCrunch’s report, Google simultaneously launched Google Antigravity, a development platform specifically designed for agentic programming, allowing developers to work at a higher, task-oriented level.

Second: From Complex Papers to Interactive Animations—Visualization Superpowers That Simplify Complexity

To test its ability to understand and transform complex information, we gave it a classic AI paper, “Attention is All You Need”, and asked it to complete three tasks:

  1. First, summarize the core concepts for non-technical readers
  2. Next, convert the summary into a two-minute YouTube video script
  3. Finally, and most impressively, design and code a standalone HTML/CSS/SVG animation to explain the complex “attention mechanism” from the paper in a visual way for beginners

The AI not only perfectly completed the summary and script but also created a concise yet profound interactive animation that clearly demonstrates how language models use “attention” to understand word relationships. The real breakthrough of this capability lies in “modality translation”—the AI fluently translates highly abstract academic text into interactive visual language. This ability to transform across different communication modes is a hallmark of higher intelligence.

“These results were impressive. The animation itself seems simple, but what it represents is extraordinary: AI can digest a PDF explaining complex concepts and transform it into a visualization that thoroughly simplifies those concepts. That’s incredibly cool.”

MIT Technology Review points out that Gemini 3 introduces “generative interfaces” functionality, allowing the model to autonomously choose the most suitable output format for prompts, assembling visual layouts and dynamic views rather than just returning text blocks.

Third: Beyond Instructions—”Creative Inference” That Proactively Builds Complete Musical Experiences

Next, we tried a more creative instruction: “Create a completely original song and make it playable in a browser.” The expectation was perhaps just a melody or some musical notation code, but Gemini 3’s results far exceeded imagination.

It not only created an original song but also built a complete web player using the tone.js library. This player included:

  • Customized music visualization effects
  • Carefully designed color schemes
  • Play/stop buttons
  • Complete lyric display

And these were never requested in the instructions by the tester. This demonstrates AI’s ability to predict “latent user needs.” It didn’t stop at literal instructions but perceived users’ unspoken desires—a complete, rich experience—thus taking a critical step from “instruction executor” to “creative collaborator.”

“The tester didn’t ask it to build visualization tools, specify colors, buttons, or even provide lyrics. Only one instruction was given: ‘Create an original song and make it playable in a browser.’ Yet it delivered a complete experience.”

This ability to creatively infer beyond instructions sets Gemini 3 apart in competition with Claude 3.7 Sonnet and ChatGPT-4.5.

Fourth: Not Just Answers, But True Multi-Step “Thinking” and Planning

Gemini 3 demonstrates remarkable depth when handling complex logic. In one test, testers asked it to act as an operations planner, scheduling the release of four YouTube videos over the next ten days while satisfying multiple ambiguous and conflicting real-world constraints.

It not only successfully created a perfect schedule but also clearly explained the trade-offs involved and proposed alternative solutions—resembling the strategic thinking of a senior executive rather than simple computation.

In another difficult probability puzzle (the Monty Hall problem), it not only calculated the correct answer but also “showed its calculation process” visually, presenting the reasoning logic step by step. This capability is crucial—it represents AI’s thinking process transforming from an opaque “black box” into a transparent partner that can be audited and trusted, forming the cornerstone of human-AI trust.

According to 9to5Google’s report, Gemini 3 has achieved state-of-the-art reasoning capabilities, with the ability to “grasp depth and nuance,” “perceive subtle clues in creative ideas, or peel apart overlapping layers of difficult problems.”

Google is also launching Gemini 3 Deep Think mode, an enhanced reasoning mode that further improves Gemini 3’s performance. In the Humanity’s Last Exam test, Deep Think achieved 41.0% accuracy (without tools), 93.8% on GPQA Diamond, and an unprecedented 45.1% on ARC-AGI (with code execution), demonstrating its ability to solve novel challenges.

Fifth: The Prototype of an AI Assistant That Can Actually Do Things for You—”Agent Mode”

One of the most exciting features is the new “Agent Mode.” In testing, the task given was: “Book a well-reviewed Italian restaurant with outdoor seating in San Francisco for tonight.”

Once activated, Gemini 3 autonomously opened a browser in the cloud, navigated to the OpenTable reservation site, searched based on the criteria, and completed the reservation process step by step, with full transparency and traceability.

This marks a fundamental paradigm shift: AI is evolving from a “knowledge engine” to an “action engine.” It’s no longer confined to a conversational sandbox but has become an agent capable of using the tools we use (browsers, websites) to accomplish tasks for us in the real digital world.

CNBC’s report notes that Google simultaneously released the Google Antigravity platform, allowing developers to code at a “higher, task-oriented level.” According to Josh Woodward, VP of Google Labs and Gemini, Gemini 3 is Google’s “best vibe coding model ever.”

This breakthrough in agentic capabilities positions Gemini 3 at a pivotal point in AI development history, forming intense competition with OpenAI’s GPT-5 and Anthropic’s Claude series.

Conclusion: We’re at the Singularity of an AI Capability Explosion

Comprehensively reviewing these tests, the impact of Gemini 3 can no longer be described as “incremental progress.” From translating academic theories into interactive art to autonomously navigating the web to complete real-world tasks, Gemini 3 demonstrates the rise of AI as a multimodal, agentic-capable partner.

What is Gemini 3? It’s not just a more powerful language model but represents a new paradigm in AI development:

  • From responding to instructions to proactive creation
  • From providing answers to executing tasks
  • From text output to multimodal experiences
  • From tool to collaborative partner

We’re witnessing a fundamental transformation in AI capabilities. It’s no longer merely processing human language but beginning to execute human intent. This prompts contemplation: if a single instruction can create a game today, what kind of future will we create with AI a year from now?

With Gemini 3 Deep Think launching soon for AI Ultra subscribers and the continued development of the Google Antigravity platform, we can expect AI-assisted development and autonomous agents to become mainstream. This will not only change how software is developed but could potentially reshape the entire paradigm of human-technology interaction.

For readers wanting to dive deeper into AI technology developments, we recommend further reading AI-Stack’s articles on AI Development Trends, Vibe Coding, and AI Agents to comprehensively understand this rapidly evolving field.

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