Large Language Models, known as LLMs, began as systems that processed text to answer questions or assist with simple tasks. At their core they read, predict and generate language in a way that feels natural for the user. Most people had their first experience with them through early ChatGPT models, but their purpose has grown far beyond that early idea.

Today the term LLM has become too broad, maybe even inadequate. It groups together tools that no longer belong in the same category. A compact model of two gigabytes running on a phone has little in common with a reasoning model with 1 trillion parameters trained for complex decisions. Calling both tools by the same name is similar to calling a pocket calculator and a research supercomputer the same device.

The change began in 2023 when most people spoke about chatbots as if they were one single concept. Within two years the picture had changed completely. Companies, research groups and independent creators began to build highly focused models for writing, problem solving, math support, security work, creative tasks and many other activities.

This guide explains how this evolution created clear types of LLMs, from ChatGPT and Gemini to specialized tools, and why understanding these groups helps people choose the right tool for their work in 2026.

What is an LLM?

A large language model is a system trained to read and produce text. It learns by processing huge amounts of written material, such as books, articles and websites. During training it studies how words usually appear together and how sentences are formed, which allows it to understand and generate language in a natural way.

When you ask a question the model predicts one word at a time until it forms a full answer. It does not store every book or article it read during training. Instead it learns patterns: which words often appear together, how sentences are structured, how ideas connect. This allows it to answer questions, write summaries and assist with tasks that involve text.

The model does not think, form opinions or understand the world the way a person does. It relies entirely on the statistical patterns it learned during training. Its strength comes from the size of the training data and the quality of the learning process. While this basic mechanism applies to all LLMs, different models use these principles in very different ways depending on their purpose and design.

Most people recognize ChatGPT, Claude and Gemini, but hundreds of specialized models now exist for specific industries like healthcare, legal work and software development. Understanding these differences helps users choose the right tool.

What are the Types of LLMs?

LLMs split into seven main categories:

• Base models serve as raw building blocks for researchers.
• Instruction tuned models like ChatGPT and Claude handle everyday tasks through conversation.
• Reasoning models think through complex problems step by step before answering.
• Small language models run entirely on your phone or laptop for privacy.
• Multimodal models work with images, audio and video alongside text.
• Mixture of experts models activate only the parts they need for each task.
• Domain specific models focus on fields like medicine or law.

Additionally, new emerging categories include hybrid systems that switch modes automatically, agentic tools that take actions beyond conversation, and long context systems that remember entire books or documents.

Base Models

Base Models are the simplest form of language model. They complete sentences by guessing what word should come next. They are trained on huge collections of public text without human guidance on what’s right or wrong. Their goal is very simple. They look at an incomplete sentence and guess the next word based on probability. They do not try to help, follow rules or think. They only continue patterns they have seen during training.

Developers use Base Models as starting points to build more useful assistants. On their own they are not good for daily use because they give answers that feel empty or random. They also fail often in tasks that require judgment, facts or empathy. This is why regular users rarely interact with them directly. Researchers love them because they are pure and flexible, but they are also risky because they can invent information and have no safety layer.

Base Models are the foundation that everything else is built from.

Key Examples: GPT-4 Base, Llama 3.1 Base.

Instruction Tuned Models

Instruction Tuned Models begin as Base Models but gain new skills through extra training. First they receive supervised fine tuning where humans show them how to answer in helpful ways. Then they go through a process called RLHF, which stands for reinforcement learning from human feedback. This reward system encourages helpful and safe responses. The final result is a model that listens and follows instructions.

These models are dependable and clear, which makes them the closest thing to a general assistant. They can explain concepts, summarize text, write drafts and guide users step by step. Their main focus is usefulness.

Nowadays, these models are the standard product for most companies. They are widely adopted, stable and predictable. Some developers mention an alignment tax, meaning these models sacrifice a small amount of creative freedom in exchange for safety and reliability.

Key Examples: Claude 3.5 Sonnet, Llama 3.1 Instruct, GPT-4o Standard Mode.Retry

Reasoning Models

Reasoning Models take time to work through problems instead of answering right away. They use a method called Chain of Thought, which means they solve the problem step by step in their internal workspace before showing you the final answer. Think of them as careful thinkers who double-check their work rather than blurting out the first response that comes to mind.

People use these models when accuracy matters more than speed. They are great for math, coding, complex planning and deep analysis. They are tnot ideal for casual chat because they take more time to respond. They trade speed for careful thinking.

In 2025 the market moved toward hybrid reasoning. These models can switch their deeper thinking on or off depending on the task. This gives users the power to choose speed or precision. Adoption is rising fast among researchers, engineers and teachers.

Key Examples: OpenAI o1, DeepSeek R1, Claude 3 point 7 Sonnet Thinking Mode.

Small Language Models

Small Language Models are designed to run on your phone or laptop instead of remote servers. They often learn from larger models through a training method called distillation, where they follow examples from a stronger teacher. Some also learn from simplified educational data that teaches core concepts clearly.

People use these models for privacy and speed. Because they run on phones and laptops, they keep all data on the device. This allows sensitive work without sending information to external servers. They are also much cheaper to operate. For users who handle confidential information or work offline frequently, these benefits outweigh having access to broader knowledge.

By 2025 these models became widely respected. They are not less intelligent, they are simply narrower in knowledge. They shine in specific tasks and everyday offline assistance. Many companies now release both large and small versions of their models.

Key Examples: Microsoft Phi-3.5, Google Gemma 2 (2B), Apple Intelligence On-Device.

Multimodal Models

Multimodal Models handle text, images, audio and video in the same conversation. They are trained from the beginning on all these formats together. This means you can upload a photo, record audio or share a video and get a response that understands all of it without switching between different tools.

People use these models when words alone are not enough. They can watch long videos, understand the tone of a conversation, analyze photos, describe scenes and support audio content. This makes them useful for education, safety, entertainment and creative work.

By 2025 these models became standard for media companies, creators and researchers. Their strength is understanding different formats at once and responding based on everything you share. Some companies now focus only on native multimodal systems.

Key Examples: Gemini 3, GPT 5 Unified Multimodal

Mixture of Experts Models

These models work like a team of specialists rather than a single generalist. Inside the model, many smaller expert networks each focus on different types of questions. A routing system decides which experts should handle each request. Only the relevant experts activate for any given task, while the rest stay idle.

People use these models because they deliver strong performance with low cost. They can store a huge amount of knowledge while keeping fast response times. They are perfect for companies that need scale without paying for full model activation.

By 2025 these models became popular in open source projects and commercial systems. Their biggest advantage is efficiency. Their main drawback is that the routing system can sometimes send questions to the wrong experts, leading to odd or inaccurate responses, though better training has reduced this issue.

Key Examples: DeepSeek V3, Mixtral, Grok 1.

Domain Specific Models

Domain Specific Models are trained on private data from a single field. This can be medicine, finance, law, engineering or any profession with specialized terminology. They focus on accuracy only within that area and do not attempt to know everything about the world.

People choose these models because general purpose tools often fail with specialized terms. A medical question or a legal contract requires extremely precise language. These models reduce errors in fields where mistakes can be very costly.

In 2025 these models saw strong adoption in hospitals, banks, research labs and law firms. They excel at understanding complex vocabulary in their field. Their weak point is that they are not helpful outside their area. They work best as partners to general models.

Key Examples: Med-Gemini, BloombergGPT, StarCoder 2.

Emerging Categories

Global spending on artificial intelligence reached more than 184 billion dollars in 2025. This investment drives rapid innovation in large language models, which is why new specialized categories continue to emerge.

• Hybrid systems act as a router that picks the right internal mode for each task. If the user wants a quick answer, the system responds with a fast model. If the user asks for a harder problem, it shifts into deeper reasoning. If the user uploads an image or audio clip, it changes again. All of this happens automatically, giving the user one smooth experience.
• Agentic systems can take actions, not just provide answers. They can call tools, fetch information, search the web, update files and complete actions inside apps. They are trained to decide when to act and when to reply with text. People use them for price checks, data collection, task automation and browsing long websites. They feel more like a real assistant that gets things done.
• Long context systems can read and remember extremely large amounts of text. Instead of working with short prompts, they can handle entire books, research papers, legal documents or long conversations. This means you can upload a full document and ask questions about any part of it, with the model remembering everything you discussed earlier. By 2025 these systems became common in research, legal work, writing and any job that needs extended memory.

Conclusion

The days of thinking that one single model can solve every problem are gone. We moved from a world of simple chat tools to a future built on many different systems, each one designed for a specific task. This new scenario offers specialized tools that work together rather than one system trying to handle everything. The change feels natural because people now expect tools that match their needs instead of one generic solution.

What does this mean for you? It’s simple. Choose the model that fits the job. If you only need a quick answer or a short email, a lighter system is enough. If your work involves planning, math or code, then a model built for deep thinking will help more. And if the task touches medicine, law or finance, do not rely on a general chat system. Pick a specialist tool trained for that field. The catalog of available models continues to grow.

The right match saves time, avoids mistakes and brings better results. Understanding the purpose of each model type is not a technical skill. It is a practical habit. Moving forward, the people and companies that gain the most will be the ones who pick the right tool for the right problem.

FAQs

Are LLMs a type of Generative AI?

Yes, large language models belong to the group of tools that create new content instead of only storing information. They can produce text, explain concepts, draft ideas and help with problem solving. They do not think like humans but they work well for language based tasks.

What type of model is ChatGPT?

It depends on the version you use. The standard version is an instruction tuned assistant built to follow directions and answer safely. More advanced modes add reasoning or multimodal abilities. This means the system can switch between quick chat, deeper logic and visual tasks.

Are agentic models better chatbots?

They are better for actions, not for simple conversation. An agentic system can search online, collect data and complete tasks inside apps. If you just want a friendly chat, a regular assistant is enough. If you want real work done, an agentic model is more useful.

Which model type is most in demand in 2025?

Instruction tuned systems remain the most widely used because they are reliable, safe and easy to understand. Companies also show strong interest in reasoning models for technical work and in agentic systems for automation. The best choice depends on the task you need to solve.

Origianl Creator: Ekaterina Pisareva
Original Link: https://justainews.com/blog/types-of-llms/
Originally Posted: Wed, 26 Nov 2025 14:53:54 +0000