Google's Gemini 3: Explained

Over the past two years, we’ve watched Google’s Gemini models evolve at a staggering pace. What began with Gemini 1’s breakthroughs in native multimodality and long-context understanding quickly expanded with Gemini 2, which laid the foundations for agentic behavior, advanced reasoning, and the first wave of truly interactive AI systems.

As someone who has worked with multimodal models, I’ve followed this progression with a mix of curiosity and skepticism, testing each generation to understand not just what it can do, but how reliably it performs for real engineering workloads.

With the arrival of Gemini 3, Google and DeepMind have signaled the beginning of a new chapter. Sundar Pichai describes it as the moment AI has progressed “from reading text and images to reading the room,” and DeepMind’s leadership calls it their most intelligent and capable model yet.

Gemini 3 Pro and its enhanced reasoning variant, Deep Think, combine multimodal perception, long-horizon reasoning, and agentic capabilities in a way that aims to bring any idea to life, not just answer prompts, but support actual building, learning, and planning.

What makes this release especially significant is that Google is deploying Gemini 3 across the entire ecosystem on day one: Search, the Gemini app, AI Studio, Vertex AI, and the new agentic development platform, Antigravity.

This is the first Gemini generation being shipped at the full scale of Google’s infrastructure, and that alone marks a major shift in how quickly developers, researchers, and everyday users can leverage its capabilities.

In this blog, I break down what Gemini 3 actually changes and understand not just what it is, but how it performs and where it fits in the rapidly evolving landscape of advanced reasoning models.

What's Better or New in Gemini 3 Pro

Gemini 3 Pro brings a strong set of improvements that make it a clear step forward from earlier Gemini models.

1. Stronger Reasoning Across Difficult Tasks

Gemini 3 Pro shows a noticeable jump in reasoning ability. It performs much better on complex benchmarks such as Humanity’s Last Exam (37.5%) and GPQA Diamond (91.9%), showing that it handles scientific, logical, and academic problems with more depth and accuracy than previous Gemini models.

2. Better Multimodal Understanding

The model is now more capable of understanding mixed inputs like text, charts, and images. Scores on tests like MMMU-Pro (81%) and Video-MMMU (87.6%) highlight how well it interprets real-world visuals, diagrams, and long information across formats.

3. Improved Coding and Multi-Step Task Execution

Gemini 3 Pro is much more reliable for coding and long workflows. It achieves higher results on LiveCodeBench (2,439 Elo) and SWE-Bench Verified (76.2%), which shows clearer code generation, debugging, and the ability to follow multi-step instructions more consistently.

4. New Deep Think Mode for Harder Problems

Deep Think is a new mode designed to improve structured reasoning. It offers stronger results on more challenging tests, such as raising the GPQA Diamond score to 93.8% and boosting difficult visual puzzle tasks like ARC-AGI-2 to 45.1% when tool use is allowed.

5. Handles Longer Context More Effectively

Gemini 3 Pro processes long documents and connected information with better stability.

Its improved long-context tests (77% on MRCR for 128k tokens) show that it can maintain coherence even across very large inputs.

6. Higher Accuracy in Everyday Understanding Tasks

The model also improves on practical tasks like OCR, question answering, and common-sense reasoning.

For example, OmniDoc OCR achieves one of the strongest results (0.115 edit distance), and Global PIQA rises to 93.4%, helping the model understand everyday scenarios more reliably.

7. Available Across Google Products From Day One

For the first time, a new Gemini model launches immediately in Search, the Gemini app, AI Studio, Vertex AI, and Antigravity.

This means users can access the new capabilities everywhere without waiting for staggered rollouts.

Benchmark Comparison: How Gemini 3 Pro Performs

Gemini 3 Pro shows a clear step forward in intelligence, reasoning, and multimodal understanding. Across almost every major benchmark, it scores significantly higher than Gemini 2.5 Pro and performs strongly against leading models like Claude Sonnet 4.5 and GPT-5.1.

Benchmark of Gemini 3 Pro

1. Academic & Scientific Reasoning

Gemini 3 Pro delivers strong results on some of the most demanding reasoning tests:

• Humanity’s Last Exam: 37.5% (tools off) - much higher than Gemini 2.5 Pro (21.6%).
• GPQA Diamond (scientific reasoning): 91.9% - one of the highest scores across all models.

These results show that Gemini 3 Pro handles deep reasoning and scientific questions with much more accuracy and stability.

2. Mathematics & Logic

Math performance improves noticeably:

• AIME 2025: 95% (tools off) and 100% (with code execution).
• MathArena Apex: 23.4% - a new high for frontier models on these extremely challenging math problems.

This means the model is better at handling step-by-step calculations, structured logic, and advanced problem-solving.

3. Multimodal Understanding

Gemini 3 Pro also performs very well on tests that combine text, images, charts, diagrams, and videos:

• MMMU-Pro: 81%
• Video-MMMU: 87.6%
• CharXiv Reasoning: 81.4%

These scores reflect stronger visual reasoning and deeper understanding of real-world multimodal tasks.

4. Coding & Agentic Capabilities

Coding and multi-step task execution show major improvements:

• LiveCodeBench: 2,439 Elo (up from 1,775).
• SWE-Bench Verified: 76.2%
• t2-bench (agentic tool use): 85.4%

These results show that Gemini 3 Pro can write better code, fix bugs more reliably, and follow complex instructions across tools.

5. Everyday Practical Tasks

Gemini 3 Pro also improves on more routine tasks:

• SimpleQA Verified: 72.1%
• Global PIQA: 93.4%
• OmniDoc OCR: best-in-class score of 0.115 edit distance

This reflects clearer understanding of factual questions, commonsense reasoning, and document interpretation.

6. Long-Context Performance

The model handles long inputs more effectively:

• MRCR (128k context): 77%
• MRCR (1M tokens): 26.3%

This helps in scenarios like long research documents, legal contracts, or large codebases.

Gemini 3 Pro with Deep think

Deep Think: Even Higher Performance

Gemini 3 Deep Think mode pushes results further on complex problems:

• Humanity’s Last Exam: 41%
• GPQA Diamond: 93.8%
• ARC-AGI-2 (visual reasoning puzzles): 45.1% with code execution

Deep Think offers a more deliberate reasoning process, ideal for tasks that require multiple layers of analysis or creative problem-solving.

Experimenting with Gemini 3

I provided Gemini Pro 3 with comprehensive instructions to create a fully functional space exploration game, designed to rigorously test its reasoning and coding capabilities.

The prompt challenged the AI to implement complex game mechanics including collision detection, procedural generation, progressive difficulty scaling, particle effects, power-up systems, and persistent high score storage, all within a single HTML file.

This multifaceted task evaluates Gemini's ability to handle intricate logic, real-time rendering, object-oriented design, performance optimization, and state management simultaneously. By requesting production-ready code with no placeholders,

I'm testing whether Gemini Pro 3 can deliver a polished, playable game that demonstrates strong problem-solving skills, attention to detail, and the ability to integrate multiple complex systems into a cohesive, engaging user experience.

Here is the result;

Then to test Gemini 3 Pro's ability to translate theoretical physics knowledge into working code, so I'm challenging it to create an interactive physics simulation that not only looks good but accurately represents the underlying mathematics of classical mechanics.

I'm specifically interested in seeing if it can handle the numerical complexity of multi-body interactions, maintain energy conservation, and create an intuitive interface that makes abstract physics concepts tangible and explorable.

This will reveal whether the AI truly understands the physics or just mimics surface-level behavior.

Let's see what it produces.

Conclusion

Gemini 3 Pro represents one of the clearest leaps forward in Google’s AI roadmap moving beyond incremental upgrades and toward a model that can genuinely reason, build, and operate as an intelligent agent.

Its performance across academic reasoning, multimodal understanding, coding, and long-context tasks shows that this generation closes much of the gap between theoretical benchmarks and real engineering demands.

What makes Gemini 3 Pro notable is not only the higher scores, but the stability and reliability these capabilities bring in practice. Whether generating production-ready games, constructing physics simulations grounded in real mathematics, or interpreting complex multimodal inputs, the model demonstrates a level of consistency that earlier versions struggled to maintain.

The addition of Deep Think extends these strengths further, offering structured, deliberate reasoning that helps on the hardest scientific, logical, and visual problems.

Combined with Google’s decision to deploy Gemini 3 across Search, AI Studio, Vertex AI, and Antigravity on day one, this release is less about a new model and more about a unified platform shift.

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