Difference between Google Gemini and Gemma

Google has developed multiple AI models to power its products, services, and developer tools. Two notable models from Google are Gemini and Gemma. While they sound similar, they serve different purposes and are designed with distinct goals in mind. This article helps you understand what each model is and highlights their key differences in a clear and beginner-friendly manner.

What is Google Gemini Model?

Google Gemini is a family of advanced, large-scale, multimodal AI models developed by Google DeepMind. These models are designed to understand and generate human-like text, images, code, audio, and video. Gemini is built to power Google’s flagship AI experiences such as Bard (now called Gemini), AI in Search, Android, and Workspace tools. It is a commercial-grade model, optimized for performance, reasoning, and scalability, often compared to OpenAI’s GPT-4.

What is Google Gemma Model?

Google Gemma is a family of lightweight, open-source AI models built by the same teams behind Gemini but intended for research, fine-tuning, and experimentation by developers. These models are optimized for local and low-resource environments like laptops, edge devices, and smaller cloud workloads. Gemma is designed with transparency and responsible AI use in mind and is freely available to the open-source community.

Gemini vs Gemma AI Model

Some of the differences are as follows:

	Google Gemini	Google Gemma
Purpose	Commercial-grade AI for advanced applications	Open-source AI for research and development
Model Size	Large-scale models (billions to trillions of parameters)	Smaller models (2B, 7B parameters)
Multimodal Capabilities	Yes (text, image, code, audio, video)	Primarily text-based
Access	Via Google products and APIs (e.g., Gemini Pro in Bard)	Freely available for download and local deployment
Deployment	Cloud-based services and enterprise tools	Runs on laptops, desktops, and edge devices
Open Source	No	Yes
Developer Use	Limited customization, focused on end-user apps	Designed for customization, fine-tuning, and research
Transparency	Closed-source with limited technical details	Fully open with detailed documentation