Google Gemini 3: Where does this generation's "most powerful brain" excel?
The real significance of the release of Gemini 3 lies in its core advantages, which are reflected in three key dimensions: task execution ability, distribution efficiency, and ecological moat. This model not only sets a new high in performance benchmark tests but, more importantly, achieves a fundamental transformation from "answering questions" to "completing tasks," capable of multi-step reasoning and automatically executing complex tasks. Additionally, Google integrated Gemini 3 into existing products such as Search and Gmail on the day of its release, reaching billions of users, which demonstrates extremely high distribution efficiency. Finally, through a full-stack approach, Google has formed an irreplicable advantage. From self-developed TPU chips, a closed-loop of massive data to a vast product matrix, these elements together build a strong ecological barrier, transforming AI from an independent tool into an execution system embedded in users' daily workflows.
On November 19, 2025, Gemini 3 was released.
Sundar Pichai wrote in Google's official blog:
Gemini 3 is our most intelligent model to date, aiming to turn any user's ideas into reality.
This time, Google didn't just release a single model. Instead, for the first time, it integrated reasoning ability, multi-modal understanding, and agent capabilities to form a comprehensive AI platform.
Upon its release, Gemini 3 went live: it was integrated into the AI Mode of Search, available on the Gemini App and AI Studio. Enterprises can deploy agents on Antigravity, and developers can use the Deep Think mode. It reaches a scale of 2 billion Search users, backed by Google's nearly $100 billion in AI investment in a year.
So, what exactly makes Gemini 3 so powerful?
By reviewing the press conference, technical demonstrations, and CEO interviews, we've distilled three dimensions:
Execution Ability - From answering questions to completing tasks. Distribution Efficiency - From independent products to system embedding. Ecological Moat - From tool upgrade to platform reconstruction.
This is the real meaning of this release.
Section 1 | Task Execution: From Answering Questions to Completing Tasks
On the day of its release, Gemini 3 set a record:
It topped the LMArena Leaderboard (the global AI model arena) with 1501 points, becoming the first model to exceed 1500 points.
(Gemini 3 Pro: Leading in most benchmark tests)
In the "Human's Final Exam," a benchmark test containing doctoral-level questions, it scored 37.5%, nearly doubling the 21.6% of the previous generation. It achieved 91.9% on GPQA Diamond (measuring doctoral-level reasoning ability) and set a new high of 23.4% on MathArena Apex for mathematical reasoning.
But what really matters behind these numbers?
1. From Benchmark Tests to Real-World Tasks
Demis Hassabis, the CEO of DeepMind, emphasized in an interview that the most significant improvement in Gemini 3 is its reasoning ability. It can conduct multi-step thinking simultaneously, while previous models often lost their train of thought and became disorganized.
What does this improvement in ability mean in practical applications?
In the demonstration of Gemini Agent, you can simply say "Organize my inbox," and it will automatically scan the email content, categorize them by importance, mark items that need a reply, draft reply suggestions, and group similar emails. The entire process doesn't require your step-by-step guidance or supervision.
Or in the atmosphere coding scenario, Hassabis mentioned that the model has crossed the threshold of practicality. In the technical demonstration, if you simply input "Create a 2D game in the style of Don't Starve where you can walk in the world and collect materials for crafting," Gemini 3 can automatically generate images in the appropriate style, a character control system, a material collection mechanism, a complete crafting interface, and directly executable code.
2. The Transformation from Conversation to Task Execution
This multi-step reasoning ability brings about a fundamental change in the way AI works.
Imagine a daily scenario: you tell the AI, "Write me a thank-you email, mention the three key points from yesterday's meeting, and attach two relevant pictures and the link to the meeting minutes."
The design goal of Gemini 3 is that you only need to say it once, and it will automatically break down the steps:
- Retrieve the meeting records
- Extract the core points
- Search for pictures from the document library
- Generate a sharing link
- Organize the content in an email tone
- Output a complete draft
In the words of Josh Woodward, the vice president of Google Labs, their goal isn't the mechanical question-and-answer mode but a more natural and intelligent conversation.
What's even more notable is that the new metric the team is now focusing on is: How many tasks can AI help you complete in a day?
It's not about how many questions it answers or how good the content it generates is, but about how many tasks it completes.
This shift in the metric reflects Google's redefinition of the boundaries of AI capabilities.
In the past, you were the commander, and AI was the soldier. You gave an order, and it executed an action.
Now, you're the boss, and AI is the assistant. You state a goal, and it figures out how to achieve it.
From the model's score of 1501, to the actual demonstration of inbox organization, to the shift in the metric of completed tasks, the strength of Gemini 3 doesn't lie in being smarter but in being able to get things done.
What Google aims to prove again isn't a higher model score but that AI can truly help you finish tasks.
Section 2 | Distribution Efficiency: Reaching Billions of Users on the Day of Release
Gemini 3 set another record:
For the first time, Google integrated the new model directly into the AI Mode of Search on the day of its release.
What does this mean? The AI Overviews in Search already reach 2 billion monthly active users, the Gemini App has over 650 million monthly active users, 13 million developers are using generative models, and 70% of cloud customers are using AI services. On the day of release, Gemini 3 started serving this large user base.
This is a completely different distribution path.
1. The Time Difference from Zero to Billions
Currently, most AI companies adopt the "independent product" model. Users need to actively visit specific websites or apps, register an account, and learn to use the interface. Whether it's ChatGPT, Claude, or other AI products, users need to actively change their usage habits.
The path of Gemini 3 is completely different. Users don't need to download a new app, register an account, or learn a new interface. They just need to open Search or a document as usual, and the AI capabilities are right there.
Josh Woodward said that what makes Google's new product features exciting is that the integration of AI is seamless, and users can get help without changing any of their habits.
2. The Specific Form of Embedding
Behind this distribution efficiency is Google's embedding of Gemini 3 into the entry points that users use every day:
In Search, when you search for "How does RNA polymerase work?", the AI Mode will instantly generate an immersive layout with interactive visualizations. Instead of giving you a bunch of links, it directly generates an interactive scientific animation using code.
In Gmail, Gemini directly helps you draft replies, understand the context, and generate suggestions in the email interface, without switching apps or copying and pasting.
In the Android system, it replaces Google Assistant and uses voice to help you complete tasks across apps. For example, if you say "Find the meeting minutes from last week's meeting with General Manager Zhang and send them to Manager Li," it will automatically search, find the file, open an email, and complete the sending.
In Docs, it quickly summarizes the document content, supplements materials, and generates charts right in the document you're editing.
Google can achieve this because these products are already in users' phones and workflows. Gemini 3 doesn't need to acquire new users; it just needs to make existing tools smarter.
The endgame of AI isn't a super app but a set of embedded capabilities.
The model is just the underlying technology. The real moat lies in those entry points that users use every day.
Section 3 | Ecological Moat: A Path Only Google Can Take
The previous two sections showed what Gemini 3 can do and how it can quickly reach users.
But there's a more crucial question: Why can Google achieve these things?
The answer is the differentiated full-stack approach mentioned by Sundar Pichai. From chips to data centers, from models to products, from users to developers, Google controls the entire chain.
1. What Others Need, Google Already Has
The contrast is obvious:
Currently, OpenAI and Anthropic still need to persuade users to download their products, rent computing power from cloud service providers, and negotiate integrations with other platforms.
Google's users are already using Gmail and Search. Google produces its own TPU chips, and its product matrix covers various scenarios in work and life.
This isn't something that can be bought with money or quickly established.
2. The Irreplicable Three-Layer Advantage
The advantages brought by this full-stack control are reflected in three levels:
The first level is autonomous computing power. OpenAI spent over $8.6 billion on computing power in the first nine months of 2025, and Anthropic purchased $30 billion in computing power from Azure. They both need to rent from cloud service providers. Google self-developed TPU, giving it control over cost and performance, which directly determines whether it can serve billions of users on the day of release.
The second level is a closed-loop of data. Search has billions of queries every day, Gmail has tens of billions of emails, and YouTube has billions of views. These data serve as both training materials and feedback for continuous optimization. Other companies either have to buy data or face copyright lawsuits.
The third level is the product matrix. Gemini 3 can test its understanding ability in Search, its generation ability in Gmail, and its agent ability in Android. Each product is a real-world verification field for its capabilities.
The combination of these three levels of advantages forms a closed-loop that other AI companies can't replicate: autonomous computing power makes large-scale deployment possible, massive data makes continuous optimization a reality, and the product matrix ensures that capability verification runs through the entire chain.
Demis Hassabis called DeepMind the engine room of Google, providing AI power for the entire Google ecosystem. And Sundar called Gemini the engine that drives the frontiers of intelligence, agents, and personalization. This means that Google isn't just creating a better AI tool but is reconstructing the underlying logic of computing.
When AI becomes the new interface layer for all digital services, the goal isn't to create a single product but to reconstruct the entire ecosystem.
This is where Gemini 3 is truly powerful: it's the only AI that billions of people can use on the day of its release.
And this is something that other AI companies find difficult to achieve.
Conclusion | Three Dimensions, One Answer
Let's return to the question in the title: What exactly makes Gemini 3 so powerful?
First, it's powerful in getting things done. It topped the leaderboard with 1501 Elo points, but more importantly, it can complete entire tasks, not just answer a single question.
Second, it's powerful in reaching users quickly. It reached billions of users on the day of release because it's embedded in tools like Gmail, Search, and Android that users use every day.
Third, it's powerful in its ecological moat. From self-developed TPU to the product matrix, Google controls the entire chain from chips to users.
These three dimensions together constitute the real strength of Gemini 3:
It's not just about having a higher score but about changing the way it's used.
AI is no longer a dialog box that you occasionally open but an execution system embedded in your daily workflow. The transformation from independent apps to embedded capabilities is becoming the new consensus in AI applications. Around the same time, Alibaba also integrated Qianwen into its search product, Quark.
The shift from the "dialog box" to the "life entry point" might be the next stop for AI.
Original Article Links:
https://www.youtube.com/watch?v=PFyccJhbQ6w
https://www.youtube.com/watch?v=rq-2i1blAlU&t=18s
https://www.youtube.com/watch?v=og7R9C_N3Zg
https://blog.google/products/gemini/gemini-3-collection/
https://blog.google/products/gemini/gemini-3/?utm_source=x&utm_medium=social&utm_campaign=&utm_content=#responsible-development
Source: Official Media/Online News
This article is from the WeChat official account "AI Deep Researcher." Author: AI Deep Researcher. Editor: Shen Si. Republished by 36Kr with permission.