What is Google Gemini?

What is Google Gemini? Inside Google’s Most Advanced AI Model

Google’s Gemini is the company’s most ambitious AI project to date: a family of multimodal foundation models designed to power everything from Search to Android.

Unlike earlier AI systems that handled only text, Gemini was built to understand and generate across text, images, audio, and video from the ground up.

A family of models

Gemini has evolved rapidly in just a couple of years, with each release bringing breakthroughs.

• Gemini 1.0 (2023): Launched in three tiers: Ultra (largest and most capable), Pro (scalable, general-purpose), and Nano (lightweight, on-device).
• Gemini 1.5 (2024): Introduced Mixture-of-Experts (MoE) architecture and a breakthrough 1 million token context window. This allowed it to analyse entire books, massive codebases, or hours of transcripts in one go.
• Gemini 2.5 Pro (2025): Current flagship “thinking model” that can allocate extra reasoning steps, call tools, and process up to 3 hours of video in a single prompt.
  This tiered design lets Gemini scale from cloud supercomputers down to smartphones. For example, Gemini Nano already runs on the Pixel 8 Pro, powering features like on-device transcription and smart replies.

How does Gemini work?

Behind the scenes, Gemini is powered by a carefully engineered architecture that balances scale, efficiency, and flexibility. Among its most important features are:

• Long context windows: Up to 1,000,000 tokens tested (128k available to most users). In benchmarks, Gemini recalled hidden facts in 1M-token documents with 99% accuracy.
• Native multimodality: Trained on text, images, audio, and video together. It outperformed GPT-4 Vision on multimodal benchmarks like MMMU, scoring 59.4% (state of the art).
• “Thinking” mode: Reinforcement learning trains Gemini to use multiple inference steps for harder problems—effectively doing chain-of-thought internally.
• Tool use: Built-in ability to call APIs and external tools, demonstrated in experiments like “Gemini Plays Pokémon.”
• Grounding in Google Search: Gemini can ground its outputs on Google’s Search index, helping it provide factual and verifiable responses by aligning with real-world information.
• Compute at scale: Trained on Google’s TPU v4–v6 (Trillium) supercomputers, optimised end-to-end for speed and efficiency.

What can Gemini do?

When put to the test, Gemini has shown remarkable versatility across knowledge, coding, and creative reasoning. For example:

• Knowledge & reasoning: Gemini Ultra was the first model to score 90.0% on MMLU, beating the human expert average (89%).
• Coding: Powers AlphaCode 2, which solved nearly twice as many competitive programming problems as its predecessor, performing better than ~85% of human coders.
• Multimodal tasks: Can describe images, analyse charts in PDFs, summarise hours of audio, and even reason about the plot of a 44-minute silent film.
• Scale to user needs: From 128k tokens for standard use to 1M tokens for premium tasks, Gemini can digest entire books, research reports, or codebases in one go.

Why Gemini matters

Gemini is not just a chatbot. It represents a step toward AI that can act as a genuine assistant. Its significance lies in the way it combines different capabilities into a single system:

• It can see, hear, and read in the same model.
• It can handle massive inputs that were impossible just a year ago.
• It can reason step by step, not just generate answers.
• It is grounded in Google’s search index for factual reliability.
• It’s built to integrate into products at every level, from Android features to enterprise APIs.
  By combining multimodality, massive scale, and efficiency, Gemini is designed as a universal AI backbone for Google’s ecosystem—and beyond.

How Gemini compares

The broader AI landscape is competitive, and Gemini inevitably draws comparisons with rivals. Each leading model has its strengths:

• OpenAI’s GPT-4.5: Excellent all-rounder with polished conversational style. Max context window of 32k tokens and supports text + images.
• Anthropic’s Claude 3 Opus: Strong on alignment and safety, with 200k tokens (1M for select users) and vision support. Known for its friendly, transparent style.
• Google Gemini 2.5 Pro: Unique in offering 1M tokens, audio, and video support, plus agent-like reasoning and tool use.
  On benchmarks, Gemini currently leads in several areas:
• MMLU: 90.0% (Gemini Ultra) vs 89.6% (GPT-4.5) vs ~84–85% (Claude 3).
• Coding (HumanEval): Gemini and GPT-4.5 both achieve ~88–90%, with Claude trailing but improving.

Google Gemini isn’t just another LLM.

Google attempts to build a universal AI system.

With cutting-edge architecture, multimodal training, grounding in Google Search, and unmatched context length, Gemini is pushing AI closer to the role of a general-purpose assistant that can read, watch, listen, and think at scale.