In 2025, large language models (LLMs) have become an integral part of our digital landscape, revolutionizing how we interact with information, solve problems, and even simulate human-like research. From powering chatbots to aiding in complex data analysis, LLMs are everywhere, but their diverse types can be confusing. Understanding these types helps us leverage their strengths for different tasks, whether it’s generating creative content, making accurate predictions, or even simulating research processes.
This article aims to break down seven key types of LLMs, exploring their basic features, training methods, applications, and limitations. By the end, you’ll clearly see how each type stands out and where they excel.
1. Base Models
Basic Features
Base models are the foundational building blocks of the LLM universe. Trained on massive unlabeled datasets, they excel at text prediction. Think of them as language experts with a broad, general knowledge but no inherent skill in following specific instructions right out of the box. They understand the structure and patterns of language deeply.
Training Process
They are trained on vast amounts of raw text data from diverse sources like the internet, books, and academic papers. There’s no fine-tuning with human feedback for instruction following at this stage; it’s all about learning the fundamental language patterns.
Applications
These models serve as the starting point for developing more specialized LLMs. For example, Llama and Mistral, two key base models, can be used as the foundation to build chatbots, content generators, or other NLP tools after further customization.
Limitations
While great at text prediction, they struggle with instruction – following tasks. They can generate text but need additional tuning to be useful for tasks like answering specific user queries in a helpful way. They lack the “understanding” of what a user intends beyond basic language generation.
2. Instruction-Tuned Models
Basic Features
Instruction – tuned models are like base models that have gone through a “refinement” process. They are base models fine-tuned with human feedback to align with user intent. So, they are designed to follow instructions, be helpful, harmless, and honest. ChatGPT and Claude are prime examples here.
Training Process
After the initial training of the base model, they undergo a second phase where human feedback is used. Annotators provide feedback on how well the model follows instructions, and the model is adjusted to better meet user needs. This includes learning to respond appropriately to different types of queries, from simple questions to complex tasks.
Applications
Widely used in chatbot applications, virtual assistants, and any scenario where following user instructions is crucial. For instance, they can be used to answer customer service questions, help with homework, or generate content based on specific prompts.
Limitations
Over-reliance on human feedback can sometimes lead to over-correction. Also, they might struggle with very niche or extremely complex instructions that are outside the scope of their training feedback. And, like all models, they can have biases present in the training data that might seep through during instruction following.
3. Reasoning Models
Basic Features
Reasoning models are trained to “think out loud” before giving a final answer. They write their thought process, which significantly improves their accuracy. This step-by-step problem-solving approach makes them stand out.
Training Process
They are trained not just on text data but also on data that encourages the model to show its reasoning. For example, datasets might include problem-solving scenarios where the thought process is laid out, and the model learns to mimic this. Claude 3.7 Sonnet with reasoning mode enabled is a good example.
Applications
These models are perfect for tasks that require complex problem-solving, like mathematical problem – solving, logical reasoning tasks, or even some types of scientific analysis where a step-by- step approach is needed.
Limitations
The process of writing out the thought process can be time-consuming, which might not be ideal for real-time, high-speed applications. Also, if the training data for reasoning is limited in scope, they might struggle with novel or extremely complex reasoning tasks outside their training.
4. Mixture of Experts (MoE)
Basic Features
Mixture of Experts (MoE) is a clever architectural twist. It allows models to scale to trillions of parameters without breaking compute budgets. The key is that it activates only the relevant “experts” per task. So, different parts of the model (experts) specialize in different types of tasks.
Training Process
The model is structured with multiple “expert” sub-models. During training, the model learns which experts are best suited for different types of tasks. For example, some experts might be good at language translation, others at text summarization. When a task comes in, only the relevant experts are activated. Qwen3-235B-A22B is a key example, with 235B total parameters but only 22B active per token via MoE (with 8 out of 128 experts active at a time).
Applications
These models are great for large-scale, multi-task NLP applications. They can handle a wide variety of tasks efficiently because they can tap into the right experts for each job. For example, in a large – scale content platform that needs translation, summarization, and sentiment analysis, an MoE model can do all these tasks efficiently.
Limitations
The complexity of the architecture can make training and debugging difficult. Also, ensuring that the right experts are activated for each task every time can be a challenge, and if there’s a misalignment, the performance can suffer.
5. Multimodal Models (MLLMs)
Basic Features
Multimodal models are the “all-sensory” LLMs. They process images, audio, and text together. This enables AI to reason over, extract information, and answer questions about visual and audio inputs along with text. GPT-4o, Claude 3 Opus, and Gemini are notable examples.
Training Process
They are trained on a combination of text, image, and audio data. The model learns to associate visual and audio inputs with text, so it can, for example, describe an image in words, transcribe audio and relate it to text, or answer questions that involve both visual and textual information.
Applications
These models are used in a wide range of applications. For example, they can be used in content moderation (analyzing images and text in social media posts), in the medical field to analyze X-rays and patient text records together, or in education to create more interactive learning materials that combine images, audio, and text.
Limitations
Training on multiple modalities is complex and requires large amounts of diverse data. Also, ensuring that the model accurately integrates and interprets different modalities can be tricky. For example, an image might be ambiguous, and the model might misinterpret it when combined with text.
6. Hybrid Models
Basic Features
Hybrid models are like the “flexible thinkers” of the LLM world. They can dynamically decide whether a prompt needs fast execution or deeper reasoning.
Training Process
They are trained to recognize different types of prompts and determine the appropriate response approach. This involves training on a variety of prompts, some that require quick answers and others that need in – depth reasoning.
Applications
These models are useful in applications where there’s a mix of simple and complex tasks. For example, in a customer service chatbot, a simple query like “What are your opening hours?” can be answered quickly, while a complex query like “How does your refund policy apply to custom orders?” can be handled with deeper reasoning. You can use the tip of including “no_think” in your system prompt if you don’t want the model to spend time “thinking” for simple tasks.
Limitations
The decision-making process of choosing between fast execution and deeper reasoning can sometimes be flawed. The model might misclassify a prompt and use the wrong approach, leading to either a rushed, inaccurate answer or an overly long, unnecessary reasoning process.
7. Deep Research Agents
Basic Features
Deep research agents are the “virtual researchers” of the LLM world. They simulate the work of a human researcher: planning, browsing the web, synthesizing information, and generating structured, detailed reports. Claude with web search and research mode is a key example.
Training Process
They are trained on data that mimics the research process. This includes datasets of research plans, web – browsing behaviors (in a simulated environment), and examples of well – structured research reports. They learn to gather information from multiple sources, evaluate its credibility, and synthesize it into a coherent report.
Applications
These models are perfect for tasks like market research, academic literature reviews, or investigative journalism. For example, a business can use a deep research agent to gather data on market trends, competitor analysis, and consumer sentiment to generate a detailed market report.
Limitations
Relying on web-based information means they are subject to the quality and biases of online sources. Also, the process of simulating human research perfectly is challenging, and there might be gaps in the depth of research or the ability to handle very specialized, niche research topics.
In conclusion, the world of LLMs in 2025 is rich and diverse, with each type of model bringing its own set of capabilities. By understanding these seven types—Base Models, Instruction – Tuned Models, Reasoning Models, Mixture of Experts (MoE), Multimodal Models (MLLMs), Hybrid Models, and Deep Research Agents—you can better choose the right tool for your specific needs, whether it’s creating a simple chatbot, analyzing complex multimodal data, or simulating in – depth research. And for all your GPU-related requirements in training, deploying, and running these LLMs, WhaleFlux stands as a reliable partner, offering a range of high-performance GPUs with flexible rental and purchase options (minimum one -month rental period), ensuring that your AI projects are executed smoothly and efficiently.
