What is Gemini 3, and how is it different from Gemini 2.5?

Gemini 3 is Google’s latest generation of the Gemini model family, designed for stronger reasoning, multimodal understanding, and agentic workflows. Compared with Gemini 2.5, Google’s own benchmarks show higher scores on coding, terminal control, and web development tasks, plus support for very long million-token contexts, so the model can work over entire codebases or large document sets at once.

What is Google Antigravity, and who is it for?

Google Antigravity is an agent-first development platform that lets AI agents work directly with your editor, terminal and browser while you supervise their plans and artefacts. It is aimed at professional developers and advanced learners who want to offload multi-step tasks like building features, fixing bugs or running experiments, while still keeping humans in charge of review and deployment.

Do I need advanced experience to use Gemini 3 in my projects?

You can start experimenting with Gemini 3 using Google AI Studio and hosted APIs with only basic programming knowledge, especially for prototyping and small tools. However, using it safely in production still requires solid fundamentals in software engineering, testing, and security, because you are responsible for deciding where the model fits, how to validate its output, and how to protect user data.

The New AI Model Wave: What Google’s Gemini 3 Means for Developers Everywhere

Updated on November 22, 2025 6 minutes read

AI developer working at dual monitors with code and a glowing neural network visual, illustrating Google Gemini 3–powered agentic workflows in a modern office.

Google has launched Gemini 3, a new generation of its Gemini AI models focused on stronger reasoning, multimodal understanding, and agent-style workflows. It is already live in AI Mode in Search, the Gemini app, Google AI Studio, Vertex AI, and other tools that developers use every day.

The first release, Gemini 3 Pro, arrives in preview with benchmark numbers that significantly surpass Gemini 2.5 Pro and a one-million-token context window for long documents and codebases. Google has also unveiled Google Antigravity, an agent-first development environment where Gemini-powered agents can work across editor, terminal, and browser.

What happened

On 18 November 2025, Google published the product blog A new era of intelligence with Gemini 3, announcing Gemini 3 as its most intelligent model and beginning the Gemini 3 era by releasing Gemini 3 Pro in preview. The model became available immediately in the Gemini app, AI Mode in Search, Google AI Studio, Vertex AI, Gemini, CLI, and the new Google Antigravity platform.

That same day, the developed post Start building with Gemini 3 explained how developers can access Gemini 3 Pro through the Gemini API, with preview pricing of 2 USD per million input tokens and 12 USD per million output tokens for prompts up to 200,000 tokens. It also highlighted performance figures such as 54.2% on Terminal-Bench 2.0 and a 1487 Elo score on WebDev Arena, plus a free but rate-limited tier in Google AI Studio.

On 19 November 2025, the Google Cloud blog confirmed Gemini 3 for enterprises via Vertex AI and Gemini Enterprise, emphasizing its one-million-token context window and multimodal capabilities. The same day, a Google Developers post described Gemini 3 Pro Preview as a core orchestrator for AI agents and announced day-zero support in frameworks such as LangChain, the AI SDK by Vercel, LlamaIndex, Pydantic AI, and n8n.

On 20 November 2025, Google introduced Google Antigravity as an agentic development platform designed to let agents autonomously plan, execute, and verify complex tasks across editor, terminal, and browser. The Antigravity desktop app launched in public preview at no cost for individuals on macOS, Windows, and Linux, with generous Gemini 3 Pro rate limits during the trial period.

Why it matters

For individual learners and junior developers, Gemini 3 changes where AI shows up: it is now embedded directly in search, a dedicated app, a nd mainstream cloud tools rather than living in a separate chatbot. Knowing how to structure prompts, interpret model output, and reason about limitations becomes a baseline skill, not a niche specialty.

For practicing engineers, the combination of stronger reasoning, agentic too, use and a one-million-token context window makes it realistic for a model to work over entire repositories, long,g logs or mixed media datasets. Tasks such as migrating legacy code, generating user interfaces, or analyzing operational metrics can be delegated to agents, while humans retain control over design, review, and deployment.

For teams, Gemini 3 plus Antigravity signals a gradual shift from “type a prompt, get a snippet” to AI systems that run multi-step workflows under policy. Organizations will need to define where agents are allowed to run commands, what must be approved by humans, and how to log and audit AI-driven changes alongside human work.

Key numbers

Gemini 3 launches into a large existing user base. Google reports that AI Overviews in Search serve around 2 billion people each month, the Gemini app has more than 650 million monthly us, and over 13 million developers have built with Gemini-family models. Those numbers give any new capability immediate reach across consumers, developers, and per enterprise.

On benchmarks, Gemini 3 Pro improves sharply over Gemini 2.5 Pro. It reaches about 1501 Elo on LMArena, 1487 Elo on WebDev Arena, and delivers higher scores on SimpleQA Verified, MMM, U-Pr, and Video-MMMU, reflecting gains in factual accuracy and multimodal reasoning. For coding and tool use, it scores 54.2% on Terminal-Bench 2.0 and shows strong performance on SWE-bench Verified, and is already being integrated into tools like Cursor, JetBrains Assistant, and other coding workflows.

Preview pricing for Gemini 3 Pro is set at 2 USD per million input tokens and 12 USD per million output tokens for prompts up to 200,000 tokens, with higher rates above that threshold. A free but rate-limited tier in Google AI Studio allows experimentation without immediate cost, while Antigravity’s free public preview and generous limits lower the barrier to trying multi-agent setups.

Context

Gemini 3 builds on two years of rapid iteration. Gemini 1 introduced native multimodality and longer context windows, while Gemini 2 and 2.5 pushed into more sophisticated reasoning and agentic behavior, with Gemini 2.5 Pro topping the LMArena leaderboard for months. The new generation is framed as a consolidation of those strands: better reasoning, stronger multimodal performance, nd APIs designed explicitly for agents.

Google’s published evaluations show Gemini 3 Pro outperforming Gemini 2.5 Pro on every major benchmark they report, from LMArena to demanding tasks like Humanity’s Last Exam and GPQA Diamond. The new Gemini 3 Deep Think mode further raises scores on tests such as ARC-AGI-2, but is being rolled out cautiously after additional safety checks.

In parallel, partners are integrating Gemini 3 into popular tools: JetBrains IDEs, GitHub Copilot, Cursor, Replit, Figma, automation platforms, and more. Many developers will encounter Gemini 3 indirectly inside these environments, even if they never call the API directly, making it part of the background infrastructure of everyday development.

What’s next

Google describes Gemini 3 as “just the start” and plans more models in the Gemini 3 series, including broader access to Gemini 3 Deep Think once testing with partners and regulators is complete. Deep Think is aimed at tasks that need deeper reasoning and multimodal analysis than the standard mode.

On the developer side, Gemini 3 introduces new API controls for reasoning depth and media handling, which give teams more control over cost, latency, and output quality. As organizations move from simple request-response calls to long-running agents, these controls become important for keeping behavior predictable and auditable.

Google Antigravity’s public preview gives individual developers a way to experiment with these ideas in a desktop app before enterprises connect them to production systems. In the near term, the practical step is to prototype focused use cases such as code migration, documentation generation, or log triage, while clearly defining boundaries and approval steps for AI-driven changes.

How to go deeper

If you want to understand what benchmarks and context windows really mean for model behavior, a structured program in data science and AI can help. Code Labs Academy’s Data Science & AI bootcamp covers statistics, Python, evaluation, and practical projects, so claims like 50% more solved tasks become numbers you can interpret rather than marketing. You can explore the curriculum via the Data Science & AI bootcamp page at:

For developers who care about how Gemini 3 plugs into real applications, a full-stack path is useful. The Web Development bootcamp links modern frontend frameworks with backend APIs and deployment, giving you hands-on experience with building and shipping AI-assisted web apps:

If your focus is on risk and resilience, the Cybersecurity bootcamp dives into secure coding, threat modeling, and incident response. Those skills are increasingly relevant as agentic systems gain access to terminals, cloudresourcesus and production data