7 Types of Large Language Models (LLMs)

Large Language Models (LLMs) have rapidly emerged as a transformative force in artificial intelligence, demonstrating remarkable capabilities in understanding, generating, and interacting with human language. From powering sophisticated chatbots and translation services to assisting in complex coding and creative writing tasks, LLMs are reshaping industries and redefining human-computer interaction. However, the term "LLM" encompasses a wide and increasingly diverse range of model types, each with unique architectures, training methodologies, strengths, and weaknesses. Understanding these distinctions is crucial for effectively leveraging their power and navigating the rapidly evolving AI landscape.

This tutorial aims to provide a comprehensive overview of several key types of LLMs that are prominent today or represent significant directions in research and development. We will delve into the fundamental characteristics, training processes, applications, and limitations of each category, offering clarity on how they differ and where their specific advantages lie.

We will begin by exploring Base Models, the foundational building blocks trained on vast amounts of unlabeled text data. These models excel at pattern recognition and language prediction but often lack the ability to follow specific instructions reliably. Building upon this foundation, we will examine Instruction-Tuned Models, which are fine-tuned using supervised learning and human feedback to better understand and execute user commands, making them more suitable for task-oriented applications like chatbots and assistants.

Next, we will investigate more specialized architectures. Mixture of Experts (MoE) Models represent a significant architectural innovation, employing multiple specialized sub-networks ("experts") and a gating mechanism to route tasks efficiently. This approach allows for dramatically larger model sizes (in terms of total parameters) while maintaining computational efficiency during training and inference, albeit with challenges related to memory requirements and fine-tuning.

We will then turn our attention to models explicitly designed for complex cognitive tasks. Reasoning Models are optimized to tackle problems requiring multi-step thought processes, such as mathematical proofs, logic puzzles, and complex planning. These models often generate intermediate steps, providing transparency into their reasoning process.

Further expanding capabilities, Multimodal Models (MLLMs) break the text-only barrier, processing and understanding information across various modalities like images, audio, and video alongside text. We will clarify how these differ fundamentally from models solely focused on generating images or video from text.

We will also explore Hybrid Models, which blend characteristics from different categories, potentially integrating diverse reasoning approaches or dynamically deciding how to process information based on complexity. Finally, we will look at Deep Research [Agents], AI agents designed for autonomous, in-depth investigation using web browsing and iterative analysis.

By exploring each of these categories, this tutorial will equip you with a clearer understanding of the diverse capabilities within the LLM ecosystem, helping you appreciate the specific strengths and applications of different model types.

Chapter 1: Base Models - The Foundation of Language Understanding

At the heart of the Large Language Model revolution lie the Base Models, often referred to as foundation models. These represent the initial, fundamental stage of LLM development, serving as the bedrock upon which more specialized and task-oriented models are built. Understanding base models is essential to grasping the core principles of how LLMs learn and function before they are adapted for specific applications like conversation or instruction following.

A base LLM can be conceptualized as the "raw" or "core" version of a language model [1]. Its primary characteristic stems from its training methodology: unsupervised learning on truly massive and diverse datasets. These datasets typically encompass vast swathes of text and code scraped from the public internet, digitized books, scientific articles, and other sources, potentially amounting to trillions of words. The key here is that the data is largely unlabeled; the model isn't explicitly told what the "correct" answer is for a given input during this phase.

Instead, base models are trained on objectives like next-token prediction or masked language modeling. In next-token prediction, the model learns to predict the most statistically probable next word (or sub-word unit, called a token) in a sequence, given the preceding context. For example, given the input "The cat sat on the...", the model learns to assign high probability to words like "mat", "chair", or "windowsill" based on the patterns it has observed in its training data. Masked language modeling involves predicting missing (masked) words within a sentence. Through these self-supervised tasks, the model implicitly learns intricate patterns of grammar, syntax, semantics, factual knowledge, and even some rudimentary reasoning abilities embedded within the language data [1, 2].

The sheer scale of the training data allows base models to develop a broad, general understanding across an incredibly wide range of topics. They become repositories of information gleaned from their training corpus, capable of generating text that is often coherent, contextually relevant, and stylistically varied [1]. However, this knowledge is statistical and pattern-based; the model doesn't "understand" in the human sense but rather excels at predicting sequences based on learned correlations.

A defining feature, and often a limitation, of base models is that they are not inherently designed to follow instructions or engage in coherent dialogue. While they can complete prompts or answer questions based on the patterns they've learned (e.g., if trained on many Q&A pairs, they might answer a question), their behavior can be unpredictable [1, 3]. They might continue a prompt in an unexpected way, generate factually incorrect information (hallucinate), or fail to adhere to specific constraints given in a prompt. Their primary goal during training was sequence prediction, not adherence to user intent. Prompt engineering for base models often requires careful crafting to steer the model towards the desired output format or content.

Despite these limitations for direct interaction, base models are incredibly valuable as foundations. Their broad knowledge and language understanding capabilities make them the ideal starting point for fine-tuning [1]. By taking a pre-trained base model and further training it on smaller, curated datasets tailored to specific tasks (like question answering, summarization, or following instructions), developers can create more specialized and reliable models, such as the instruction-tuned models we will discuss in the next chapter.

In summary, base LLMs are characterized by:

• Unsupervised Pre-training: Trained on vast, unlabeled text/code datasets.
• Core Objective: Typically next-token prediction or masked language modeling.
• Broad Knowledge: Develop general understanding across many topics from data patterns.
• Limited Instruction Following: Not inherently designed to follow user commands reliably.
• Foundation Role: Serve as the starting point for fine-tuning into specialized models.

Their applications in their raw form might include generating creative text variations, exploring language patterns, or acting as a knowledge base where precise instruction following isn't paramount. However, their most significant impact lies in enabling the development of more sophisticated, fine-tuned models that power many of the AI applications we interact with daily.

References: [1] Toloka Team. (2024, November 19). Base LLM vs. instruction-tuned LLM. Toloka Blog. Retrieved May 2, 2025, from https://toloka.ai/blog/base-llm-vs-instruction-tuned-llm/ [2] AWS. What is LLM? - Large Language Models Explained. Amazon Web Services. Retrieved May 2, 2025, from https://aws.amazon.com/what-is/large-language-model/ [3] Boutnaru, S. (2025, February 9). The Artificial Intelligence Journey — Base LLM (Base Large Language Model). Medium. Retrieved May 2, 2025, from https://medium.com/@boutnaru/the-artificial-intelligence-journey-base-llm-base-large-language-model-726423106b14

Chapter 2: Instruction-Tuned Models - Aligning LLMs with User Intent

While base models possess vast knowledge, their inherent nature as sequence predictors makes them unreliable for tasks requiring specific actions or adherence to user commands. To bridge this gap and create more practical, interactive AI systems, the concept of Instruction-Tuned Models was developed. These models represent a crucial evolution, taking a pre-trained base model and refining it specifically to understand and follow human instructions effectively [1].

Instruction tuning is a form of supervised fine-tuning (SFT) applied after the initial unsupervised pre-training phase. Instead of just predicting the next token, the model is trained on a dataset composed of explicit instruction-prompt-response pairs [1]. These pairs demonstrate the desired behavior for various tasks. For example, the dataset might contain examples like:

• Instruction: "Summarize the following text."
• Prompt: "[Lengthy article text]"
• Response: "[Concise summary of the article]"

Or:

• Instruction: "Translate this sentence to French."
• Prompt: "Hello, how are you?"
• Response: "Bonjour, comment ça va?"

This dataset is often curated through significant human effort, involving labelers writing instructions, prompts, and high-quality responses. Increasingly, techniques like Reinforcement Learning from Human Feedback (RLHF) or AI Feedback (RLAIF) are also employed. In RLHF, human reviewers rank different model outputs for the same prompt, and this feedback is used to train a reward model, which then guides the LLM's fine-tuning via reinforcement learning to produce outputs that align better with human preferences [4]. This combined SFT and RLHF/RLAIF process helps the model learn not just what information to provide, but how to provide it in a helpful, harmless, and honest manner, aligning it more closely with user intent [1].

The primary benefit of instruction tuning is a marked improvement in the model's ability to follow complex, multi-step instructions without deviating [1]. Unlike base models that might ramble or misunderstand the core task, instruction-tuned models are trained to interpret the user's command and generate a relevant, structured response. They become significantly better at tasks requiring specific formats (like creating lists or writing code), adhering to constraints (like tone or length), and understanding the nuances of user requests [1].

Key characteristics that distinguish instruction-tuned models include:

• Improved Instruction Following: They are explicitly trained to understand and execute commands, leading to more reliable and predictable behavior [1].
• Enhanced Task Specialization: They excel at specific NLP tasks they were fine-tuned on, such as summarization, translation, question answering, code generation, and structured content creation [1].
• Better User Intent Understanding: The fine-tuning process makes them more adept at grasping the underlying goal of a user's prompt, even if not perfectly phrased [1].
• Controllability: Users have more control over the output's style, tone, and format through instructions.
• Safety and Alignment: Fine-tuning often incorporates safety measures and alignment techniques to reduce harmful, biased, or untruthful outputs.

Instruction-tuned models power many of the LLM applications commonly used today, including advanced chatbots like ChatGPT, Google Gemini, and Anthropic's Claude. Their applications are vast and continue to expand:

• Conversational AI: Engaging in coherent, helpful dialogue, answering questions, and providing assistance.
• Content Creation: Generating articles, marketing copy, emails, stories, and other creative text formats based on specific instructions.
• Coding Assistance: Generating, explaining, debugging, and translating code snippets.
• Educational Tools: Providing explanations, tutoring, and answering student queries.
• Data Analysis and Reporting: Summarizing data, generating insights, and creating structured reports [1].

While instruction tuning significantly enhances usability and reliability, it's important to note that these models still inherit the knowledge (and potential biases) of their underlying base model. They are not immune to generating incorrect information (hallucinations), but the fine-tuning process generally makes them more grounded and less prone to unpredictable outputs compared to raw base models.

In essence, instruction tuning transforms a knowledgeable but unguided base model into a helpful and capable assistant, aligning its vast linguistic capabilities with the specific needs and intentions of human users.

References: [1] Toloka Team. (2024, November 19). Base LLM vs. instruction-tuned LLM. Toloka Blog. Retrieved May 2, 2025, from https://toloka.ai/blog/base-llm-vs-instruction-tuned-llm/ [4] OpenAI. (2024, September 12). Learning to reason with LLMs. OpenAI Blog. Retrieved May 2, 2025, from https://openai.com/index/learning-to-reason-with-llms/ (Implicit reference to RLHF/RLAIF in reasoning model development, applicable concept here)

Chapter 3: Mixture of Experts (MoE) Models - Scaling Efficiently

As the demand for more powerful and knowledgeable Large Language Models grows, researchers continually seek ways to increase model size without incurring prohibitive computational costs during training and inference. One of the most promising architectural innovations addressing this challenge is the Mixture of Experts (MoE) model. MoE represents a significant departure from traditional "dense" architectures, enabling models to scale to trillions of parameters while maintaining relative efficiency [5].

In a standard dense transformer model, every input token is processed by all parameters in each layer, particularly the feed-forward network (FFN) layers which constitute a large portion of the model's parameters. This means the computational cost scales directly with the model size. MoE introduces the concept of sparsity or conditional computation to overcome this limitation [5].

Instead of dense FFN layers, MoE models incorporate specialized MoE layers. Each MoE layer consists of two primary components [5]:
 

1. Multiple Experts: A set of smaller, independent neural networks (typically FFNs themselves, though they could be more complex). Each expert can be thought of as specializing in different types of data or tasks, although this specialization often emerges implicitly during training rather than being explicitly assigned.
2. Gating Network (Router): A small neural network that acts as a traffic controller. For each input token arriving at the MoE layer, the gating network dynamically decides which expert(s) should process that token. It calculates scores for each expert based on the token's representation and typically selects the top-k experts (where k is often 1 or 2) to handle the computation [5].

The core idea is that for any given token, only a small fraction of the total parameters within the MoE layer (i.e., the parameters of the selected expert(s)) are activated and used for computation. The outputs from the activated expert(s) are then typically combined, often weighted by the scores assigned by the gating network, before being passed to the next layer [5]. It's crucial to note that while the FFN layers are replaced by sparse MoE layers, other components of the transformer, like the attention mechanisms, are usually shared across all tokens, similar to dense models. This is why a model like Mixtral 8x7B, despite having 8 experts of 7B parameters each in its MoE layers, has a total parameter count closer to 47B rather than 56B (8x7B) – the non-FFN parameters are shared [5].

This sparse activation leads to significant benefits:

• Efficient Pre-training: MoE models can achieve comparable quality to dense models with substantially less computational cost during pre-training. This allows researchers to train much larger models (in terms of total parameters) or use larger datasets within the same compute budget [5].
• Faster Inference: Although an MoE model might have a very large number of total parameters, the actual number of computations (FLOPs) required per token during inference only depends on the parameters of the activated experts (and the shared parameters). For example, Mixtral 8x7B, using 2 experts per token, has an inference speed roughly equivalent to a 12-14B parameter dense model, not a 47B or 56B one [5].

However, the MoE architecture also introduces unique challenges:

• High Memory Requirements: Despite the computational efficiency, all parameters (including all experts) must be loaded into the GPU memory (VRAM) during inference. This means an MoE model requires significantly more VRAM than a dense model with equivalent inference FLOPs [5].
• Training Instability and Load Balancing: Ensuring that tokens are distributed relatively evenly across experts (load balancing) is critical for efficient training and preventing experts from becoming over- or under-utilized. Sophisticated loss functions and routing strategies are often needed to manage this [5].
• Fine-tuning Difficulties: MoE models have historically been more challenging to fine-tune effectively compared to dense models, sometimes exhibiting tendencies towards overfitting. However, recent advancements in instruction-tuning techniques for MoEs are showing promise [5].
• Communication Overhead: In distributed training or inference setups, routing tokens to the correct experts across different devices can introduce communication bottlenecks.

Prominent examples of MoE models include Llama 4 Scout (109B-A17B) and Alibaba’s Qwen3-235B-A22B. In Qwen3-235B-A22B, "Qwen3" designates the third generation of the model, "235B" indicates the total number of parameters, and "A22B" means that only 22 billion parameters are active per token via a Mixture-of-Experts design (8 out of 128 experts per token). This approach achieves scalability and efficiency by activating only a subset of the model for each input, allowing for dense-model-level quality with reduced computational cost. Qwen3 exemplifies how sparse activation and expert routing enable large-scale models to be both powerful and relatively efficient.

References: [5] Sanseviero, O., Tunstall, L., Schmid, P., Mangrulkar, S., Belkada, Y., & Cuenca, P. (2023, December 11). Mixture of Experts Explained. Hugging Face Blog. Retrieved May 2, 2025, from https://huggingface.co/blog/moe

Chapter 4: Reasoning Models - Enhancing Complex Problem-Solving

While instruction-tuned models significantly improve an LLM's ability to follow commands and perform specific tasks, many real-world problems require more than just direct execution; they demand complex, multi-step thinking, logical deduction, and the ability to plan and execute a sequence of operations. This is where Reasoning Models come into play. These are LLMs that have been specifically enhanced or designed to excel at tasks requiring intricate, step-by-step problem-solving [6].

Reasoning, in this context, refers to the process of tackling questions or problems that necessitate intermediate steps to arrive at a correct solution [6]. Simple factual recall (e.g., "What is the capital of France?") doesn't typically require reasoning. However, solving a mathematical word problem, debugging complex code, navigating a logic puzzle, or planning a multi-stage project involves breaking the problem down, applying rules or principles, and synthesizing information through a sequence of steps. While most modern instruction-tuned LLMs possess some basic reasoning capabilities learned implicitly during pre-training or fine-tuning, dedicated reasoning models are optimized to handle significantly higher levels of complexity [6].

A key characteristic often associated with reasoning models is their ability to generate or utilize intermediate steps, sometimes referred to as a "chain of thought" or "scratchpad" [6, 9]. These intermediate steps can manifest in two ways:

1. Explicit Reasoning: The model includes the steps of its reasoning process directly within its output, showing its work much like a student solving a math problem. This provides transparency and allows users (or developers) to potentially identify errors in the reasoning path.
2. Implicit Reasoning: The model performs multiple internal iterations or calculations, generating intermediate thoughts or results that are not necessarily shown to the end-user but are used internally to arrive at the final answer [6]. OpenAI's o1 model is suggested to operate partly in this manner [4, 6].

Enhancing the reasoning capabilities of LLMs involves several distinct approaches, often used in combination [6]:

• Inference-Time Techniques: These methods don't alter the underlying model but change how it's prompted or how its outputs are generated during inference. Techniques like Chain-of-Thought (CoT) prompting explicitly ask the model to "think step-by-step." Self-Consistency involves generating multiple reasoning paths and selecting the most consistent answer through majority voting. Tree-of-Thoughts (ToT) explores multiple reasoning paths concurrently, evaluating intermediate steps like searching through a tree [6, 9]. These techniques essentially allocate more computational resources at inference time to improve reasoning quality.
• Specialized Training Data: Fine-tuning models (using SFT) on datasets specifically designed to teach reasoning is crucial. These datasets might include mathematical problems with step-by-step solutions, logical deduction exercises, code with explanations, or complex instruction-following tasks [6].
• Reinforcement Learning: Similar to instruction tuning, RL (often RLHF or RLAIF) can be used with reward models specifically designed to incentivize correct reasoning steps (process supervision) or accurate final outcomes resulting from complex reasoning (outcome supervision) [4, 6]. Models like DeepSeek-R1 utilize extensive RL training for reasoning [6].
• Distillation: Smaller models can be trained to mimic the reasoning outputs of larger, more capable reasoning models, effectively distilling the reasoning capability into a more efficient package [6].

Reasoning models are particularly well-suited for tasks where accuracy hinges on logical coherence and multi-step processing [6]:

• Advanced Mathematics: Solving complex equations, proofs, and word problems.
• Logic Puzzles and Games: Navigating riddles, strategic games, and constraint satisfaction problems.
• Scientific Reasoning: Formulating hypotheses, designing experiments, interpreting data.
• Complex Code Generation and Debugging: Understanding intricate program logic, identifying bugs, generating complex algorithms.
• Planning and Scheduling: Breaking down complex goals into actionable steps.

However, this specialization comes with trade-offs [6]:

• Computational Cost: Both training specialized reasoning models and employing inference-time reasoning techniques can be computationally expensive.
• Verbosity and Efficiency: For simple tasks not requiring deep reasoning, these models might be overly verbose or less efficient than standard instruction-tuned models.
• Potential for Plausible Errors: Reasoning models can sometimes generate convincing-looking but ultimately incorrect reasoning paths ("overthinking" or sophisticated hallucination).

The development of reasoning models represents a significant step towards more capable and versatile AI systems, pushing LLMs beyond simple pattern matching and instruction following towards more human-like problem-solving abilities.

References: [4] OpenAI. (2024, September 12). Learning to reason with LLMs. OpenAI Blog. Retrieved May 2, 2025, from https://openai.com/index/learning-to-reason-with-llms/ [6] Raschka, S. (2025, February 5). Understanding Reasoning LLMs. Ahead of AI. Retrieved May 2, 2025, from https://sebastianraschka.com/blog/2025/understanding-reasoning-llms.html [9] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903. (General reference for CoT)

Chapter 5: Multimodal Models - Understanding Beyond Text

The world is inherently multimodal; humans perceive and interact with information through various senses – sight, sound, touch – often simultaneously. Traditional Large Language Models, however, primarily operate within the realm of text. Multimodal Large Language Models (MLLMs) represent a significant leap forward, designed to process, understand, and even generate information across multiple data types or modalities, such as text, images, audio, and video [7]. This capability allows them to engage with information in a way that more closely mirrors human perception and enables a wider range of more complex applications.

MLLMs expand upon the foundations laid by traditional LLMs. While they often leverage a powerful pre-trained LLM as their backbone for language understanding and reasoning, they incorporate additional components to handle non-textual data [7]. The core architectural difference lies in the need for specialized encoders for each modality. For instance, an MLLM might use a Vision Transformer (ViT) or a Convolutional Neural Network (CNN) to process images, an audio encoder for sound, and the standard LLM tokenizer/embedding layer for text. These encoders transform the input from each modality into vector representations (embeddings) [7].

A crucial step in MLLM architecture is embedding alignment and fusion. The embeddings generated by the different modality encoders need to be projected into a shared space where the model can understand the relationships between them. A dedicated fusion module or specific training techniques (like contrastive learning) are employed to integrate these diverse representations into a unified multimodal understanding [7]. This allows the model, for example, to connect the word "dog" in a text caption to the visual features of a dog in an accompanying image.

The general workflow often involves [7]:

1. Ingestion and Encoding: Receiving input across multiple modalities (e.g., an image and a text question about it) and processing each through its respective encoder.
2. Alignment and Fusion: Projecting and combining the different embeddings into a unified representation.
3. Cross-Modal Learning/Reasoning: Processing the fused representation, often using the LLM backbone, to understand the relationships and context across modalities.
4. Output Generation: Producing an output, which could be text (e.g., answering the question about the image), but potentially also another modality depending on the model's architecture and training (though text output is most common for current MLLMs focused on understanding).

This ability to process combined inputs leads to powerful capabilities beyond text-only models:

• Rich Data Interpretation: Analyzing documents containing text, charts, and images; understanding videos with audio and visual elements.
• Cross-Modal Reasoning: Answering detailed questions about images or videos, describing visual scenes, explaining audio events.
• Enhanced Interaction: Enabling more natural human-AI interaction, such as discussing a shared visual context.

However, building and training MLLMs presents significant challenges [7]:

• Architectural Complexity: Designing effective encoders and fusion mechanisms is difficult.
• Training Data: Requires massive, diverse datasets pairing different modalities (e.g., image-caption pairs, video-transcript pairs).
• Computational Cost: Training these complex models with large parameter counts and diverse data is computationally intensive.
• Alignment: Ensuring meaningful alignment between representations from different modalities remains an active area of research.

Distinguishing MLLMs from Text-to-Image/Video Models:

It is vital to differentiate MLLMs from models primarily focused on generating one modality from another, such as text-to-image models (e.g., Stable Diffusion, Midjourney) or text-to-video models (e.g., Sora). While both involve multiple modalities, their core purpose differs significantly [7]:

• Text-to-Image/Video Models: These are primarily generative specialists. Their main function is to synthesize high-fidelity visual content (images or video frames) based solely on a textual description. They excel at translating text prompts into pixel data but typically lack deep understanding or reasoning capabilities about the input modalities beyond what's needed for generation. They take text in and produce images/video out.
• Multimodal LLMs (MLLMs): These models prioritize cross-modal understanding and reasoning. They are designed to take multiple modalities as input (e.g., image + text, video + audio + text) and perform tasks that require comprehending the relationship between these inputs. Their output is often textual (e.g., describing an image, answering a question about a video), reflecting their focus on understanding and explanation, although future MLLMs might generate outputs in various modalities more frequently. Their strength lies in interpreting and reasoning about combined multimodal data.

In essence, while a text-to-image model creates a picture from a description, an MLLM can look at a picture and a description (or question) and reason about them together. Models like Google's Gemini and OpenAI's GPT-4V are prominent examples of MLLMs focused on understanding and reasoning across text and images.

References: [7] NVIDIA. What Are Multimodal Large Language Models? NVIDIA Glossary. Retrieved May 2, 2025, from https://www.nvidia.com/en-us/glossary/multimodal-large-language-models/

Chapter 6: Hybrid Models - Integrating Diverse Capabilities

The landscape of Large Language Models is not strictly defined by mutually exclusive categories. As research progresses, models are emerging that blend characteristics from different types, leading to the concept of Hybrid Models. While the term "hybrid" can be applied broadly, in the context of our discussion, we focus on models that integrate different reasoning approaches or dynamically decide how to process information based on the input's complexity or nature, particularly touching upon the user's query about models deciding whether to use reasoning.

The need for hybrid approaches arises from the observation that no single model architecture or training paradigm is optimal for all tasks. Simple queries might be best handled by efficient instruction-tuned models, while complex problems demand the sophisticated multi-step processing of reasoning models. A hybrid model aligns with the user's query and makes dynamic reasoning selection. Such a model might possess multiple internal pathways or modules optimized for different levels of cognitive load:

• Fast Pathway: For simple, routine queries, the model might use a computationally cheaper, faster processing route, perhaps akin to a standard instruction-tuned response mechanism.
• Deep Reasoning Pathway: When the model detects complexity, ambiguity, contradictions, or specific triggers indicating a need for careful analysis (e.g., mathematical symbols, logical operators, planning requests), it could activate a more resource-intensive reasoning module. This module might employ techniques like chain-of-thought, self-correction, or even call external tools or specialized sub-models [6, 8].

The decision mechanism itself could be a learned component, perhaps a gating network similar to those in MoE models, but routing tasks based on complexity rather than just token identity. Alternatively, it could be triggered by specific prompt structures or internal confidence scores.

Note: Remember to include the "no_think" in your system prompt if you don't want model to spend time "thinking". Letting the model to engage in elaborate reasoning when it already produces equally good results without it will significantly increase both cost and response time.

While models explicitly marketed as "hybrid reasoning selectors" are not yet commonplace. 
Another way how people may use hybrid model is involving data from multiple modalities, requiring the capabilities of MLLMs. It involves the integration of different types of reasoning or processing within a single system. For instance, research explores combining symbolic reasoning (like mathematical logic or rule-based systems) with the pattern-matching strengths of neural networks. An LLM might handle the natural language understanding and common-sense aspects of a problem, while interfacing with a symbolic solver for precise calculations or logical deductions.

The study on hybrid reasoning for autonomous driving provides a concrete example, although focused on combining reasoning types and modalities rather than dynamically choosing whether to reason [8]. In this context, the LLM acts as a central processing unit integrating diverse inputs: visual data (detected objects), sensor readings (speed, distance), and contextual knowledge (traffic laws, physics). It applies both common-sense reasoning (interpreting the driving scene) and potentially arithmetic reasoning (calculating safe distances) to make driving decisions (like brake/throttle control) [8]. This demonstrates a hybrid approach by fusing different data streams and reasoning forms to tackle a complex, dynamic task.

Hybrid models represent a move towards more adaptive and efficient AI. By dynamically allocating computational resources and selecting appropriate processing strategies based on the task at hand, they promise to combine the breadth of knowledge from large models with the specialized capabilities needed for complex reasoning and interaction, potentially deciding on-the-fly whether a simple response or a deep, reasoned analysis is required.

References: [6] Raschka, S. (2025, February 5). Understanding Reasoning LLMs. Ahead of AI. Retrieved May 2, 2025, from https://sebastianraschka.com/blog/2025/understanding-reasoning-llms.html [8] Azarafza, M., Nayyeri, M., Steinmetz, C., Staab, S., & Rettberg, A. (2024). Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving. arXiv preprint arXiv:2402.13602. [10] Olteanu, A. (2025, February 5). OpenAI's Deep Research: A Guide With Practical Examples. DataCamp Blog. Retrieved May 2, 2025, from https://www.datacamp.com/blog/deep-research-openai

Chapter 7: Deep Research- AI Agents for In-Depth Investigation

Beyond models focused on specific cognitive abilities like reasoning or multimodal understanding, a new category is emerging: Deep Research or AI Research Agents. These systems represent a shift towards more autonomous AI, designed specifically to conduct complex, multi-step research tasks by leveraging LLMs, web browsing, tool use, and iterative refinement [10]. OpenAI's "Deep Research" agent, powered by a version of their o3 model, is a prime example of this category [10].

Unlike standard LLM interactions (even those with browsing capabilities) which typically provide relatively quick, single-turn responses based on limited web searches, deep research agents are built for sustained investigation. They aim to tackle complex queries that require synthesizing information from numerous sources, cross-referencing data, and producing structured, comprehensive outputs, much like a human researcher would [10]. Think of tasks like compiling a detailed market analysis report, comparing complex products based on diverse criteria, or summarizing the state-of-the-art in a scientific field – tasks that demand more than a simple search query.

The core functionality of these models revolves around an iterative research process [10]:

1. Query Understanding and Planning: Upon receiving a research query, the agent often starts by clarifying the scope and objectives with the user. It then formulates a plan, breaking down the research task into smaller, manageable steps.
2. Information Gathering (Tool Use): The agent autonomously uses tools, primarily web browsers, to search for relevant information online. It may access dozens or even hundreds of sources.
3. Analysis and Synthesis: The agent reads and analyzes the gathered information, extracting key points, identifying patterns, comparing data across sources, and potentially using other tools (like code interpreters for data analysis) to process the findings.
4. Iterative Refinement: The process is often iterative. Based on initial findings, the agent might refine its search queries, seek out additional sources, or revisit previous steps to deepen its understanding or resolve conflicting information.
5. Report Generation: Finally, the agent synthesizes its findings into a structured, often well-cited report, presenting the information in a coherent and organized manner.

These models build upon advancements in reasoning capabilities but are specifically optimized for the context of web browsing and real-world data analysis [10]. Their training often involves reinforcement learning focused on successful execution of complex browsing and reasoning tasks, teaching them how to navigate the web effectively, evaluate source credibility (to some extent), and synthesize disparate information [10].

Key characteristics distinguishing deep research agents include:

• Autonomy and Iteration: They operate with a higher degree of autonomy, performing multiple steps over an extended period (minutes rather than seconds) to complete a research task.
• Extensive Tool Use: Heavy reliance on web browsing is fundamental, potentially augmented by other tools for calculation, data analysis, or code execution.
• Focus on Synthesis: The primary goal is not just information retrieval but deep analysis and synthesis across multiple sources.
• Structured Output: They typically aim to produce comprehensive, structured reports rather than brief answers.

Deep research agents show significant promise in benchmarks designed to test complex, real-world tasks requiring reasoning, tool use, and knowledge retrieval, such as GAIA (General AI Agent benchmark) and Humanity’s Last Exam [10]. Their performance often improves the more they are allowed to iterate and use their tools, highlighting the value of their multi-step approach [10].

Potential applications are broad, targeting anyone needing in-depth research [10]:

• Professionals: Generating market reports, competitive analyses, policy summaries.
• Researchers and Students: Literature reviews, gathering data from diverse online sources.
• Consumers: Detailed product comparisons for high-stakes purchases.
• Journalists and Analysts: Fact-checking, background research, multi-source insight generation.

However, these models are still in early stages. They can still produce incorrect facts or inferences (hallucinations), and their ability to critically evaluate source quality is an ongoing challenge. Users need to treat their outputs as highly sophisticated drafts requiring human review and verification, rather than infallible final reports [10]. Nonetheless, deep research represent a powerful new direction, moving LLMs towards becoming more capable and autonomous assistants for complex knowledge work.

References: [10] Olteanu, A. (2025, February 5). OpenAI's Deep Research: A Guide With Practical Examples. DataCamp Blog. Retrieved May 2, 2025, from https://www.datacamp.com/blog/deep-research-openai

Conclusion: The Evolving Ecosystem of Language Models

This exploration into the diverse types of Large Language Models highlights the rapid evolution and specialization occurring within the field of artificial intelligence. From the foundational Base Models trained on vast unlabeled text, we have seen the development of Instruction-Tuned Models designed for better user alignment and task execution. Architectural innovations like Mixture of Experts (MoE) demonstrate pathways to scale models efficiently, while dedicated Reasoning Models push the boundaries of complex problem-solving.

Furthermore, the ability to understand the world beyond text is captured by Multimodal Models (MLLMs), which integrate information from images, audio, and video, distinguishing them clearly from purely generative text-to-image or text-to-video systems. The emergence of Hybrid Models suggests a future where AI systems dynamically adapt their processing strategies, potentially choosing between fast responses and deep reasoning based on task complexity. Finally, Deep Research Agents showcase the potential for LLMs to act as autonomous agents, conducting in-depth investigations and synthesizing knowledge from extensive online sources.

Understanding these different categories is crucial for anyone looking to leverage LLMs effectively. Each type possesses unique strengths, weaknesses, training requirements, and ideal use cases. A base model might suffice for exploring language patterns, while a complex planning task demands a reasoning model. Analyzing a chart within a document requires multimodal capabilities, and scaling to extreme parameter counts might necessitate an MoE architecture. Choosing the right type of model, or understanding the capabilities of a given model, depends heavily on the specific task and desired outcome.

The field continues to advance at an unprecedented pace. The lines between these categories may blur further as new architectures and training techniques emerge, combining features in novel ways. However, the fundamental principles underlying these different approaches – unsupervised learning, supervised fine-tuning, reinforcement learning, sparsity, multimodality, reasoning processes, and agentic behavior – will likely remain key building blocks for future generations of AI. As these models become increasingly integrated into various aspects of our lives, a clear understanding of their diverse forms and functions will be essential for navigating the future of artificial intelligence.
 

References

[1] Toloka Team. (2024, November 19). Base LLM vs. instruction-tuned LLM. Toloka Blog. Retrieved May 2, 2025, from https://toloka.ai/blog/base-llm-vs-instruction-tuned-llm/

[2] AWS. What is LLM? - Large Language Models Explained. Amazon Web Services. Retrieved May 2, 2025, from https://aws.amazon.com/what-is/large-language-model/

[3] Boutnaru, S. (2025, February 9). The Artificial Intelligence Journey — Base LLM (Base Large Language Model). Medium. Retrieved May 2, 2025, from https://medium.com/@boutnaru/the-artificial-intelligence-journey-base-llm-base-large-language-model-726423106b14

[4] OpenAI. (2024, September 12). Learning to reason with LLMs. OpenAI Blog. Retrieved May 2, 2025, from https://openai.com/index/learning-to-reason-with-llms/

[5] Sanseviero, O., Tunstall, L., Schmid, P., Mangrulkar, S., Belkada, Y., & Cuenca, P. (2023, December 11). Mixture of Experts Explained. Hugging Face Blog. Retrieved May 2, 2025, from https://huggingface.co/blog/moe

[6] Raschka, S. (2025, February 5). Understanding Reasoning LLMs. Ahead of AI. Retrieved May 2, 2025, from https://sebastianraschka.com/blog/2025/understanding-reasoning-llms.html

[7] NVIDIA. What Are Multimodal Large Language Models? NVIDIA Glossary. Retrieved May 2, 2025, from https://www.nvidia.com/en-us/glossary/multimodal-large-language-models/

[8] Azarafza, M., Nayyeri, M., Steinmetz, C., Staab, S., & Rettberg, A. (2024). Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving. arXiv preprint arXiv:2402.13602.

[9] Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903.

[10] Olteanu, A. (2025, February 5). OpenAI's Deep Research: A Guide With Practical Examples. DataCamp Blog. Retrieved May 2, 2025, from https://www.datacamp.com/blog/deep-research-openai