After weeks of leaded videos of Gemini 3, the wait is over. Gemini 3 is here. And what can I say, when the whole AI world is raving about it. Sam Altman, himself praised Google for the release:
The release of Gemini 3 marks a turning point in how Google wants people to use AI. The company is no longer talking about tools. It is talking about assistance, reasoning and full task completion. The update brings a sense of maturity that earlier versions could not deliver.
The new model feels more confident, more capable and more flexible. It can understand almost anything you give it. The result is an AI system that feels less like a chatbot and more like a collaborator. This article walks you through what Gemini 3 is, what it can do, where you can use it and why the update matters for you!
What is Gemini 3?
Gemini 3 is Google’s next generation family of AI models built to understand text, images, audio, video and code. The system brings deeper reasoning, better memory handling and stronger awareness of user intent.
The model is designed as a unified intelligence layer for Google products. It powers Search in AI Mode, the Gemini app, Vertex AI, Firebase AI Logic and a growing set of developer and enterprise tools. The design goal is simple. One model that works everywhere and adapts to every use case.
You can think of Gemini 3 as a multimodal problem solver that learns from context and builds structured, human friendly outputs.
Key Features of Gemini 3
- Multimodal reasoning: Gemini 3 processes images, videos, PDFs, diagrams and large documents with stronger accuracy. It combines different inputs and produces insights that feel more grounded.
- Improved logic and planning: The model performs better in tasks that need clear steps, long term tracking and error checking. It can break down complex problems and stick to a plan.
- Generative UI: The system can create layouts, charts, interactive blocks and visual structures inside its response. These elements make long answers easier to read and interact with.
- Better conversational quality: Responses feel sharper and more organised. The model stays on topic and maintains context over longer conversations.
- Developer and product integration: Developers can use Gemini 3 through APIs, Vertex AI, Firebase AI Logic and the Gemini CLI. The model helps build apps, auto generate components and drive agents that handle multi step operations.
- Enterprise grade reliability: The system supports large context windows and multimodal input pipelines. It is suitable for internal knowledge systems, long document processing, operational analysis and automated workflows.
- Safer behaviour: Gemini 3 includes stronger grounding, improved fact checking signals and better protection against misleading outputs.
How to Access Gemini 3?
You can access Gemini 3 in the following ways:
Through the Google AI Studio
Google AI Studio also supports Gemini 3. You can test multimodal inputs, explore agentic behaviors and export working code directly to their applications. This is one of the simplest ways to experiment with the new model without writing much setup code.
Inside the Gemini app
The app now runs on Gemini 3. Users with higher tiers can select Thinking mode for longer, deeper responses. The experience feels more structured and more expressive.
Through Search in AI Mode
Search has a new AI Mode where you can switch to a Gemini 3 powered view. This mode can explain, summarise, plan and create structured answers that go beyond simple search results.
Through Vertex AI
Developers and teams can access Gemini 3 Pro inside Vertex AI. This gives high throughout, large context support and multimodal features. It is ideal for building production applications. You can find API costing here.
Through the Gemini CLI
The Gemini CLI allows developers to interact with Gemini 3 directly from the terminal. You can generate code, scaffold apps, analyse files and run agent level tasks without leaving your workflow. Find details here.
Performance and Benchmarks
Gemini 3 pro shows clear gains in reasoning, accuracy and multimodal performance. The model outperforms 2.5 Pro across every major benchmark and takes the top position on the LMArena Leaderboard with a score of 1501 Elo.
It handles advanced reasoning with ease. Gemini 3 Pro scores 37.5 percent on Humanity’s Last Exam and reaches 91.9 percent on GPQA Diamond. It also sets a new record in mathematics with 23.4 percent on MathArena Apex.
Its multimodal results are strong as well. The model reaches 81 percent on MMMU Pro and 87.6 percent on Video MMMU, showing strong understanding of images and video. It also improves factual accuracy with a 72.1 percent score on SimpleQA Verified.
Benchmarks never show the whole story. Real value becomes clear when you test the model across long sessions, large inputs and complex workflows. So let’s try out the model!
Trying Out Gemini 3 Pro
I am accessing Gemini 3 pro using Google AI Studio. You can use it via any of the methods mentioned previously in the article.
Task 1: Summarization
For this task, I will be uploading the SRTs of 1 hour long Free course on building a Deep Research Agent. Now I will ask Gemini 3 pro to answer some of my questions regarding the course:
Prompt: “Go through the course SRTs and do the following task:
– Which tools are covered in the course
– What are the top 7 key takeaways from this course.
– Who should consider enrolling in this course.
– One of the SRT has the overview of the entire agent building process. Give me a diagram explaining the same.“
Output:
My Take:
Gemini 3 Pro did a stellar job. I had been struggling to summarise these SRTs for a long time. I tried Gemini 2.5 Pro and GPT 5.1 earlier, but the length of the transcripts made it impossible to get accurate results. Gemini 3 Pro not only responded quickly, it produced a much more logical summary that matched the flow of the course content.
The project workflow was another challenge I had been stuck on. Image generation models kept failing to render the text correctly because the layout was too complex. Gemini 3 Pro handled it in seconds and the output came out great. See it for yourself:
Task 2: Building an App
I will be vibe coding a guess the word app for this task. I have done it previously using GPT 4.5 and GPT 5.1 as well. Find the results in my article – OpenAI Releases GPT 5.1: Here’s How it Performs! Let’s see what Gemini 3 pro creates:
Prompt: “Create a cute and interactive UI for a “Guess the Word” game where the player knows a secret word and provides 3 short clues (max 10 letters each). The AI then has 3 attempts to guess the word. If the AI guesses correctly, it wins; otherwise, the player wins.“
Output:
My Take:
The UI Gemini 3 Pro generated is way better than the one I got from GPT 5.1. The design looks cleaner, more polished and more consistent. The components are well structured and the overall layout feels intentional. What impressed me most is how quickly it came together. I only had to give a single prompt and it took shape with almost no follow up instructions. This feels like a genuine upgrade in practical UI generation.
Task 3: Reasoning
Another common problem with most LLMs is their image analysis capability. They often struggle to read complex images and usually respond incorrectly. So I wanted to see if Gemini 3 Pro could finally solve this.
Input Image:
Output:
My Take:
Wow. I genuinely did not expect Gemini 3 Pro to get this right, but it did. The response took slightly longer than GPT 5.1, but the answer was accurate. I have tested this exact image on several LLMs before and every single one failed to interpret it correctly. Gemini 3 Pro is the first model that understood it and responded with the right answer.
Enterprise Use
Gemini 3 is designed with enterprise needs in mind. Teams can use it for policy summarisation, compliance checks, long document analysis, customer support workflows and internal knowledge agents. The model handles large volumes of text, images, diagrams and reports with consistent quality.
The system also respects governance and security requirements. It allows businesses to keep data private while running complex AI workloads. Teams can connect Gemini 3 to internal systems, data warehouses and workflow engines to build practical solutions.
Large organisations that deal with research, operations, risk, procurement or documentation heavy processes will gain the most benefits.
CLI Experience
The Gemini CLI gives developers a fast way to work with the model. It supports app scaffolding, code review, documentation generation and reasoning tasks. The flow feels smooth because everything happens inside the terminal.
Developers can run a prompt that generates a full project structure. They can upload a file to analyse a bug. They can ask the model to write tests or propose improvements. The CLI makes Gemini 3 feel like a coding partner rather than a distant API.
We will be covering this in detail soon. Stay tuned!
Conclusion
When I started writing this article, I did not expect Gemini 3 to be this powerful. Most model updates feel small and barely noticeable to the end user. This one does not. Google has delivered something that genuinely feels new. I test multiple LLMs every day just out of curiosity, but I have not enjoyed exploring a model as much as I enjoyed working with Gemini 3. It feels sharp, capable and reliable in a way that stands out from everything else I have used so far.
All I can say is this. Gemini 3 is actually the best AI model in the world and to feel its power, you must try it out for yourself.
Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.
Login to continue reading and enjoy expert-curated content.
