Exploring the Different Types of Generative AI: Complete Guide
The emergence of mass-produced generative AI technologies is rightfully considered one of the most significant innovations of the 2020s. The proliferation of different types of generative AI has dramatically simplified and accelerated the creation of all types of content — from website copy to professional music or films. In this article, you'll learn about different types of Gen AI, the latest models, and their capabilities.
Introduction to Generative AI
Generative AI is a type of artificial intelligence (AI) algorithm capable of generating new content based on text queries or existing materials. This includes specialized and multimodal models — the latter can create multiple content formats (text, images, video, etc.).
The first attempts to create Gen AI prototypes began in the 1960s, but real progress only came in 2014. It was then that the first generative AI models were developed — Generative Adversarial Networks (GANs) with the ability to generate images, video, and audio.
A new wave of development for this technology began in 2022, when the release of the ChatGPT AI chatbot made it accessible to a mass audience. Since then, generative models have been continuously improving and gaining new capabilities, increasing their popularity and adoption.
AI content creation offers enormous potential for both individuals and businesses. It enables professionals, companies, and organizations to streamline workflows, automate repetitive tasks, and significantly improve their productivity.
According to a McKinsey study, one-third of companies today regularly use AI algorithms in at least one area of their business. Gartner predicts that by 2026, over 80% of enterprises will use Gen AI applications, either directly or through API integration.
The key business advantages of this technology include:
- Increased productivity. Generative AI models quickly and efficiently produce, process, and publish content (numeric expressions, text, video, audio, images, code, etc.).
- Boosted creativity. AI algorithms can create an unlimited number of content variations on a given topic, generate ideas for brainstorming sessions, and solve other creative problems.
- Improving customer experience. Chatbots and other Gen AI-enabled programs facilitate and accelerate communication between businesses and customers. They also help customers find information, generate content, and handle other requests.
- Personalization. AI models easily personalize new or existing content based on user interaction history, preferences, demographics, and regional characteristics.
Generative AI has evolved into a variety of specialized solutions tailored to specific content formats. Below, we'll explore the key types of generative AI, how they differ, and where they're used.
Text-Based Generative AI
NLP (natural language processing) models are one of the most common types of generative AI. They use the Transformer deep learning architecture, which allows them to find relationships between different parts of the data and use them to generate relevant results.
During training, these Gen AI algorithms process and analyze massive amounts of text data — developers train them on entire libraries. With internet access, they can update their data, keeping it up-to-date.
Modern text-based generative models are capable of performing a wide range of tasks related to text. For example, they create posts and articles for websites and other resources, ads and other advertising (copywriting), reports, summaries, and even works of fiction (screenplays, prose, poetry).
Their use in online chatbots allows businesses to automate communication and customer support in real time. A separate branch of Gen AI algorithms is used for code generation — they create, supplement, edit, and analyze program code in various programming languages and frameworks.
Current models:
- GPT-5.2. OpenAI's multimodal model. It performs complex reasoning, precise analysis, and excels at solving multi-step problems. GPT-5.2 efficiently processes text, code, images, and audio, demonstrating deep understanding. It is being implemented in AI assistants, agent-based systems, analytics platforms, multilingual Q&A solutions, and more.
- Meta Llama 4. A multimodal open-weight model with 17 billion active parameters demonstrates high performance in analytical reasoning, multilingual content creation, and code generation. Llama 4 is used in the Meta AI assistant, integrated into WhatsApp, Messenger, Instagram, and other Meta products.
- Mistral Large 3. A multimodal, multilingual, open-weight model from Mistral AI with a Mixture-of-Experts architecture and 41 billion active parameters. Its primary functions are text generation, programming, and data analysis. The model boasts advanced reasoning and long-term context processing capabilities, enabling its effective use in AI assistants, enterprise systems, and complex application scenarios.
- Google Gemini 3 Flash. Google's multimodal model, working with text, images, audio, and video. It offers high speed and accuracy of generation and handles complex problems effectively. This model excels at strategic planning and creative scenarios. Gemini 3 Flash has become the new standard in Google Search and the Gemini app.
- Claude Opus 4.5. The new version of Anthropic's model is focused on generating large, complex texts, data analysis, and coding. It is used for handling multi-task scenarios and developing AI agents, as it delivers consistent performance and accuracy even in long reasoning chains.
- Grok 4.1. The newest multimodal version of the Grok 4 family from xAI is designed for processing text, images, infographics, and video. The model solves NLP tasks and complex multiprocess scenarios, performs coding and mathematical calculations, and interacts with real-world systems. Version 4.1 demonstrates improved accuracy and speed compared to its predecessor.
Image and Video Generative AI
Image and video generation models are among the most in-demand types of generative AI models. They create visual content based on text queries (text-to-image, text-to-video) or user-uploaded materials (image-to-image, image-to-video, video-to-video).
Each of these AI models is trained on large datasets of visual data, comprising millions of curated images and videos. When processing a query, the algorithms search the dataset for relevant materials, then combine and adapt them to meet the user's requirements.
These Gen AI models continue to evolve and grow more sophisticated with each new version. Many of them are now capable of creating images and short videos from scratch, generating animations, extending video length, and adding professional special effects.
The most popular video and image generation AI models include MidJourney V7, Runway Gen-4.5, and Sora 2. They help automate video production tasks across industries, including advertising, design, film, vlogging, and more.
MidJourney V7
Released in April 2025, the latest version of the renowned AI image generator offers powerful tools for customizing and personalizing the images it generates. It can generate high-quality images from text or other images. MidJourney V7 also allows you to create characters in different settings, create mood boards, develop product concepts from sketches, create references in different styles, design branded posts, and more.
The AI generator can also transform static images into dynamic 5-second videos. It uses MidJourney's native video generation model for this purpose.
Runway Gen-4.5
This updated version of one of the most well-known generative AI video models generates videos and images based on visual references and text instructions. The model features improved temporal consistency and motion physics. It also offers advanced customization options for style, mood, and cinematic details in the generated content.
Runway Gen-4.5 allows you to use characters, objects, and locations in all scenes, as well as regenerate them from different positions and angles. With this model, virtually any user can create professional-quality video content — without fine-tuning or additional training.
Sora 2
Released in September 2025, OpenAI's Sora 2 is the most advanced AI model for generating video, audio, and static graphics. It is capable of generating extremely complex and dynamic scenes with remarkable realism and physical accuracy.
Users can choose from a variety of styles for their videos: realistic, cinematic, or anime. Sora also creates professional voiceovers for content, including character speech, sound effects, and ambient sound.
Audio and Music Generative AI
Generative audio AI models are capable of synthesizing music, sound effects, speech, and other audio content. Like other AI models, they store large amounts of relevant data, processing and combining it to produce new materials.
Modern AI models can generate realistic speech in hundreds of languages and dialects with a variety of tones, timbres, and intonations. They excel at replicating the structure and sound of existing musical compositions, and producing original professional tracks. By integrating with AI video-making models, they create professional voiceovers for videos, clips, and short films, selecting the right voices, background music, and sound effects.
Speech and music AI tools have broad applications. They are widely used in the music industry, game development, blogging, and podcasts. They are used to voice audiobooks, chatbots, virtual assistants, and other AI-powered applications.
The most popular AI models of this type are:
- Lyria 2. Developed by Google DeepMind, it can create professional music and audio effects both offline and in real time. It delivers professional 48 kHz stereo sound, ready for integration into any project. It provides extensive creative control.
- AudioCraft. Meta's AI model generates high-quality music and sound effects, as well as processes existing compositions. It achieves this through three built-in models: MusicGen, AudioGen, and EnCodec. It also includes an open-source codebase for developing and training new models.
- Stable Audio 2.5. The latest model from Stability AI creates studio-quality audio content up to 3 minutes long. It supports multiple input formats: text-to-audio, audio-to-audio, and audio embedding. It lets you choose the style and genre of your tracks, as well as manage other settings.
Future of Generative AI: Opportunities and Challenges
The rise and widespread adoption of various types of generative AI creates exciting opportunities to radically transform how we use artificial intelligence in the near future. However, the benefits of this technology are inextricably linked to its equally significant challenges. Let's explore the key trends and challenges.
AI Agents
One of the key innovations in this space will be specialized AI agents that autonomously and proactively perform multiple tasks. The widespread adoption of such solutions will allow businesses and individuals to automate complex multistep processes.
Energy-Efficient and Sustainable AI
Another growing trend is the focus on energy efficiency and sustainability in the development and operation of generative AI. As AI models become more powerful, their energy footprint increases, requiring developers to optimize energy consumption without compromising the performance of their products.
Industry-Specific AI Models
Many companies and organizations don't use publicly available AI models, but instead create custom solutions. Their algorithms are trained on specific (often confidential) data and offer highly specialized functionality relevant to a specific industry. Looking ahead, specialized AI models are expected to become the norm across many fields — from finance, medicine, and law to public administration and the military.
AI and Copyright
The future of generative AI may be complicated by numerous lawsuits and subsequent legislative restrictions related to copyright. Gen AI essentially creates new content based on existing materials, many of which have their own copyright holders. This calls into question the legality of registering AI products as intellectual property and the overall legality of their production and distribution.
False Information and Malicious Content
Gen AI tools can be deliberately used to create and disseminate false information to mislead audiences (for example, through deepfakes). Another serious threat is that these technologies can be used to generate malicious content — from biased or offensive texts, images, and videos to deliberately false news, erroneous instructions, and so on.
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