What is Gemini?

Gemini is Google’s multimodal AI offering. Developed by Google DeepMind, it is a “family” of models capable of understanding and generating text, images, audio, video and code. 

Originally introduced in 2023 as Gemini 1.0, DeepMind at the time noted that the model, “was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video.”

Since then, Gemini has progressed through a number of upgrades and versions. The latest Gemini 2.5 models, announced in March 2025 and currently in preview status, are being touted for their advanced reasoning capabilities. Meanwhile, in May 2025, Google announced early access to Gemini 2.5 Pro Preview (I/O edition), an updated version of 2.5 Pro featuring “significantly improved capabilities for coding, especially building compelling interactive web apps.” 

Gemini is available for Google users across various platforms, including Google Workspace apps and Maps. It can be accessed on PCs, Android devices and other mobile devices.

Here’s a closer look at Gemini AI, including some of its key features and benefits. We’ll also detail popular use cases, including some industry-specific cases and capabilities.

What are some of the key capabilities and features of Gemini? 

Gemini, like other large language models (LLMs), has been pre-trained and fine-tuned with “vast” amounts of data. Gemini datasets include diverse information, such as:

• a variety of public, proprietary and licensed audio, images and videos
• a set of codebases
• text in different languages

Similar to other multimodal AI models, such as Alibaba Cloud’s Qwen, Gemini powers a range of applications, including generative AI chatbots, productivity tools and developer platforms. 

Google Gemini has been incorporated into a wide range of Google’s apps and products, including its Google mobile phones and it is easily accessible on multiple devices for individual and business users, organizations, enterprises and developers alike. 

One of Gemini’s standout capabilities as a multimodal model is its ability to simultaneously process and reason multiple data types (text, images, audio, video, code). For example, Gemini can generate and understand content across numerous formats without converting everything to text first. The latest model includes a new API that supports real-time audio and video streaming input, enabling developers to build dynamic, interactive applications that combine various modalities and tools. 

Other noted capabilities include:

• Scalable model sizes: Available in Ultra, Pro and Nano versions – optimized for everything from complex research tasks to efficient on-device use
• Advanced reasoning: For in-depth problem solving, mathematical reasoning, coding and creative collaboration
• Large context window: Gemini Advanced can process up to 1 million tokens (about 1,500 pages of text or 30,000 lines of code) at once, enabling deep research and analysis, including through Gemini’s Deep Research feature

Common applications and use cases

Any of the Gemini models are especially useful for various multimodal tasks, ranging from text and image understanding to transcribing speech and captioning videos in real-time. Some of the most common use cases for Gemini for both individual users and organizations include: 

Content creation: Gemini can generate human-like text, translate languages, create multimedia presentations and add images or tables to documents. 

Research and analysis: Gemini has advanced capabilities for summarizing long reports, analyzing large datasets and providing deep insights.

Agentic AI features: These allow Gemini to act autonomously on behalf of users, plan multi-step tasks and interact with external systems such as Google Search and Maps.

Coding assistance: The Pro version makes additional advancements beyond UI-focused development, with improvements extending to other coding tasks such as code transformation, code editing and developing complex agentic workflows. 

Data visualization: Gemini can process and visualize large amounts of data from spreadsheets and CSV files. 

Video generation: Gemini has the ability to convert text prompts into short videos.

Industry-specific use cases for Gemini AI

A wide range of industries and sectors are incorporating Gemini’s capabilities into various functions and tasks, transforming their work. For many of these industry-specific tasks or services, Gemini AI is used in conjunction with other Google products, including Google Workspace. Some of the most popular use cases are described below. 

Banking and Financial Industry

In the banking, financial services and insurance industries, Gemini is being leveraged to create personalized and predictive experiences through AI-enhanced chatbots. Other client-facing use cases include customer onboarding and insurance claims processing.

Internally, Gemini can be used for in-depth and secure financial research, including processing vast volumes of financial and other data. It’s also making it faster and more efficient for maintaining accurate client documentation, like customer service records. 

Business and Professional Services

For various business consultants and professional organizations, Gemini is being used to improve communications, including email communications via Google Workspace. The model’s ability to quickly translate into multiple languages is crucial for global companies. 

Gemini can also speed up internal operations processes through tasks like:

• Advanced document search
• Fast and efficient video transcription
• Summaries generated from long documents and videos 

Limitations of Gemini that must be considered 

Google notes that their research aimed at upgrades and improvements continues in all aspects of the various Gemini models, including addressing potential limitations with LLMs and multimodal AI. 

Some of the potential areas of concern that Google addresses include the following: 

• Accuracy: Gemini’s responses might be inaccurate, especially when asked about complex or factual topics
• Bias: Gemini’s responses might reflect biases present in its training data
• Multiple perspectives: Gemini’s responses might fail to show a range of views
• Persona: Gemini’s responses might incorrectly suggest it has personal opinions or feelings
• False positives and false negatives: Gemini might not respond to some appropriate prompts and provide inappropriate responses to others
• Vulnerability to adversarial prompting: Users will find ways to stress test Gemini with nonsensical prompts or questions rarely asked in the real world

The future of Gemini AI: Innovation and responsibility

Gemini AI represents a significant advancement in multimodal artificial intelligence, empowering individuals, businesses and developers with robust tools that seamlessly combine text, image, audio, video and coding capabilities. As its models continue to evolve, users can expect Gemini’s innovative features to further expand their possibilities across various applications, from complex coding tasks to advanced research and creative projects.

However, like all powerful technologies, responsible use is paramount. Users should remain aware of Gemini’s limitations—such as accuracy, potential biases and vulnerabilities—and adopt best practices to mitigate risks. Ultimately, Gemini’s continued refinement promises to shape how we interact, create and innovate in our increasingly interconnected world.