Gemini, Google’s new Artificial Intelligence explained in 5 key facts

Gemini, Google's new Artificial Intelligence explained in 5 key facts
Gemini, Google's new Artificial Intelligence is considered a powerful tool with the potential to revolutionize our interaction with technology

Gemini, Google's new Artificial Intelligence explained in 5 key facts

December 6, 2023, will be a special day in Google’s history, as it marks the launch of Gemini, their largest Artificial Intelligence (AI) model to date.

Through their corporate blog, Sundar Pichai, CEO of Google and Alphabet, announced the launch of the first stage of this still-developing project. Gemini aims not only to compete but also to surpass OpenAI’s ChatGPT.

What is Gemini?

Google’s Gemini is a large-scale factual language model trained on a massive dataset of text and code. Developed by Google DeepMind, it represents a multimodal and flexible approach designed to enhance the integration of technology into everyday life and business development. Notably, Gemini can manage and analyze information efficiently across multiple formats, including text, code, audio, image, and video. It is already considered a rival to OpenAI’s GPT-4 language model.

Gemini is larger than GPT-4, boasting 1.6 trillion parameters compared to GPT-4’s 1.5 trillion. This means it has a greater capacity to learn and understand language.

Why is so relevant to Google and AI?

Gemini is significant for Google for several reasons. Firstly, it’s a powerful tool that can enhance a wide range of products and services. For instance, it could improve the accuracy and naturalness of Google’s virtual assistants, like Google Assistant and Chat, and enhance the quality of translations in Google Translate.

Secondly, it represents a significant advancement in AI development. As a large-scale factual language model, it’s trained on a massive dataset of text and code, giving it unprecedented language learning and understanding capabilities.

This is considered a powerful tool with the potential to revolutionize our interaction with technology.

Specific examples of how Gemini can optimize Google’s products and services include:

  • Virtual assistants: Enhancing Google’s virtual assistants’ ability to understand and respond to user queries.
    Automatic translators: Translating languages more accurately and fluidly, even for those with vastly different grammatical structures.
  • Search products: Improving Google search results by generating more comprehensive and accurate summaries or providing more relevant content recommendations.
  • Other companies and organizations could use it to create more natural chatbots, generate more original creative content, or even write more efficient code.

What is Gemini used for?

An advanced assistant in areas like science, finance, and programming. Its ability to understand, explain, and generate high-quality code in popular programming languages makes it a fundamental tool in the coding world.

  • Text generation
  • Language translation
  • Creative content writing
  • Informative question answering
  • Creating more natural chatbots
  • Generating original creative content
  • Efficient code writing
  • Improving search results
  • Developing new products and services

Additionally, its integration into Google products like the digital assistant Bard and the Pixel 8 Pro smartphone indicates its practical utility in everyday and business applications.


The different versions of Gemini, the AI model developed by Google DeepMind, are designed to cater to various uses and device capabilities. These versions include:

Gemini 1.0

  • Parameters: 1.6 trillion
  • Training dataset: 1.56 trillion words
  • Launch date: December 2023
  • Execution: In the cloud

Gemini Nano

  • Parameters: 100 billion
  • Training dataset: 1.56 trillion words
  • Launch date: December 2023
  • Execution: On the device

Gemini Ultra

  • An experimental version still under development, undergoing testing and trustworthiness checks with a select group of specialists.
  • Parameters: Significantly more than previous versions, at 100 trillion.
  • Execution: Optimized for complex multimodal reasoning processes.
  • Gradual release to select customers.

Gemini Pro

  • Parameters: Up to 100 trillion
  • Training dataset: Customized
  • Launch date: 2023
  • Execution: In the cloud
  • Cost: Subscription-based

Gemini AI Versions Comparison

Feature Gemini 1.0 Gemini Nano Gemini Pro Gemini Ultra
Number of Parameters 1.6 trillion 100 billion Up to 100 trillion 100 trillion
Training Data Set 1.56 trillion words 1.56 trillion words Customized 100 trillion words
Launch Date December 2023 December 2023 2023 2025
Execution In the cloud On the device In the cloud In the cloud
Cost Free Free Subscription-based —-

Source: Google

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Differences with Bard

The main differences between Gemini and Bard are:

  • Size: Gemini is significantly larger than Bard. Gemini 1.0 has 1.6 trillion parameters, while Bard has 137 billion.
  • Training dataset: Gemini is trained on a massive dataset of text and code, while Bard is trained on text only.
  • Multimodal capability: Gemini is multimodal, meaning it can understand and generate text, code, images, and audio. Bard specializes in generating text.
  • Execution: Gemini runs in the cloud, while Bard can run both in the cloud and on devices, making
  • In summary, Gemini is a more powerful language model than Bard, with a larger size, more comprehensive training dataset, and multimodal capabilities. However, Bard is more efficient for applications requiring quick and efficient processing.


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