blog details

Google Gemini that Possibly is the Next-Generation Language Model

Along with AI competition getting fierce than ever, Google has recently been continuing to expand its LLM AI family. Currently, it has Palm, Bard, Gemini, Gemma AI models under Google family, which is designed to work across various Google products, including search, ads, and Bard.

In this article, we will attempt to walk through Google Gemini family LLM AI models, and Psee what’s their pros and cons which can facilitate you select and deploy for your business demand.

Table of Contents: Google Gemini

What is Gemini AI and What it can do

Google Gemini is next-gen Generative AI model family. The whole model family is developed by Google’s AI research labs DeepMind and Google Research. It comes in three options:

  • Gemini Ultra, the flagship Gemini model.
  • Gemini Pro, a lite version of Gemini model.
  • Gemini Nano, a smaller language model.

All Gemini models have been trained to be multimodal. In other words, they are able to work with and use more than just text and words. They have been pretrained and fine-tuned on a variety of audio, images and videos, a large set of codebases and text in different languages.

However, they are different with models such as Google’s own LaMDA, which was trained exclusively on text data. LaMDA can’t understand or generate anything other than text, but models from Gemini family can do.

Gemini Pro

Gemini Pro is a significant version to Google’s AI capabilities, which offers a balance between scalability and performance. It is part of the Google Gemini AI family, which includes the Ultra and Nano versions. Gemini Pro is designed to be used for a variety of different tasks and is now available for developers and enterprises to build with.

Gemini Pro is a multimodal model as well, which means it can understand and work with text, images, audio, videos, and code. It has been fine-tuned for more advanced reasoning, planning, and understanding, making it a versatile and powerful tool for a wide range of applications..

Furthermore, Developers and enterprises can access Gemini Pro through Google AI Studio and Vertex AI. It is also available via the Gemini API, which allows developers to build Gemini-powered search and conversational agents in a low code environment. Gemini Pro is also available on Vertex AI, Google Cloud’s end-to-end AI platform, enabling developers to create production-grade AI agents in hours and days.

Gemini Pro has demonstrated strong performance on research benchmarks, outperforming other similarly-sized models. It comes with a 32K context window for text, and future versions will have an even larger context window, further enhancing its capabilities.

Gemini Ultra

Gemini Ultra is the most powerful and advanced version from Google large language model family, It is optimized for highly complex tasks and capable of reasoning seamlessly across text, images, video, audio, and code. Gemini Ultra is built from the same code as Gemini Pro and Gemini Nano, but it is designed for different use cases. It is the largest model and is optimized for the most complex tasks.

Gemini Ultra is the largest model designed for the most complex tasks. In LLM benchmarks like MMLU, Big-Bench Hard, and HumanEval, it outperformed GPT-4, and in multimodal benchmarks like MMMU, VQAv2, and MathVista, it outperformed GPT-4V.

As you can from the following comparison table between Google Gemini Ultra and GPT4, Google Gemini Ultra has unique product value propositions and stand out from many performances. It has been proved from so many practical cases as well.

blog detail

Gemini Nano

Gemini Nano is a small and agile language smodel, which is designed for ease of use and rapid model development. It features a simplified user interface and automated workflows that make it accessible even for non-technical users.

Gemini Nano prioritizes speed and flexibility, enabling users to quickly iterate through models and explore different data scenarios. However, it may have limitations when dealing with highly complex data or sophisticated modeling requirements.

Gemini Nano is ideal for rapid prototyping, exploratory analysis, and use cases where simplicity and efficiency are paramount. For instance, Google has announced that Google Pixel 8 would be enquipped with Gemini Nano and featuring performance would be enhanced

Google Gemma

Different with the other 3 models from Google Gemini LLM family, Google Gemma presents a compelling offering in the realm of lightweight, open-source language models.

Gemma models are designed to be smaller and more resource-efficient compared to other state-of-the-art models, making them suitable for deployment on various platforms, including mobile devices and edge computing environments

Gemma offers flexibility by supporting popular frameworks like TensorFlow, PyTorch, JAX, and Hugging Face Transformers, allowing developers to leverage their preferred tools. Google prioritizes responsible AI principles in Gemma’s development. Techniques like automated filtering and human feedback reinforcement learning (RLHF) aim to mitigate biases and promote responsible model behavior.

The limitation from Gemma is Gemma’s current focus is on text-to-text generation tasks. Integration with other NLP functionalities like sentiment analysis or question answering might require additional exploration.

Gemini AI Price

Similar with OpenAI and ChatGPT4’s pricing model, Google Gemini also three main pricing plan. One is Google Gemini Ultra, which is a monthly-bass subscription price. The price is $20 Per Month For Gemini Ultra. The 2nd one is a pay-as-you-go model which is from Google Gemini Pro API for developers and enterprises. Details are the image as follows:

blog detail

Last but not least, Google Gemini also offers free tier from both Gemini API and Gemini AI studio, which is for any users and developers to play around


All models have their own pros and cons, and it very depends on your actual needs, purpose and business objective. For the upcoming new features, we believe it would have more to come and people can benefit from the booming AI development and AI fierce competition

shape shape

Join our newsletter!

Get Exclusive Auto-style Content Updates & Offers

Don't worry, we won't send spams

Related Tutorials

Google Cloud

Managing Machine Learning Projects with Google Cloud

Discover how business professionals can use machine learning to solve problems, identify opportunities, and drive impact. Learn from Google Cloud experts.

Google Cloud

Customer Experiences with Contact Center AI - Dialogflow ES

Learn how to design customer conversations using Contact Center Artificial Intelligence (CCAI). Create virtual agents and test them using the simulator. Add functionality to access data from external systems, making virtual agents conversationally dynamic.