What Are the Different Types of Large Language Models?

GMI Cloud Blog | AI Infrastructure Guide | gmicloud.ai

Large language models fall into six main types: base models, instruction-tuned models, chat-aligned models, reasoning models, code models, and multimodal models. Each type is built for different tasks, behaves differently, and has different infrastructure requirements.

Understanding which type does what helps you choose the right model for your project, whether you're building a chatbot, generating code, or running research experiments. This guide explains each type with current examples.

For running LLMs at scale, GPU infrastructure from providers like GMI Cloud delivers the VRAM and bandwidth these models demand, alongside a model library covering image, video, and audio inference.

Type 1: Base Models

A base model is trained on massive text datasets to predict the next word. It learns language patterns, facts, and reasoning ability from this process, but it hasn't been taught to follow instructions or have conversations.

How it behaves: You give it a text prompt, and it continues the text. Ask it a question, and it might generate more questions instead of answering. It's a powerful text completion engine, not an assistant.

Examples: Llama 3 base, GPT-3 (original), Mistral base.

Use cases: Foundation for building other model types. Researchers use base models as starting points for fine-tuning. They're rarely used directly in applications.

Type 2: Instruction-Tuned Models

Take a base model and fine-tune it on instruction-response pairs ("Summarize this article" → summary, "Translate this to French" → translation). The result is a model that follows user instructions.

How it behaves: You give it a task, and it attempts to complete it. It follows directions but may not always be helpful, safe, or well-calibrated in tone.

Examples: Llama 3 Instruct, Mistral Instruct, FLAN-T5.

Use cases: Task execution (summarization, translation, extraction). Good for applications where the input format is predictable and the task is well-defined.

Type 3: Chat-Aligned Models (RLHF)

Take an instruction-tuned model and further refine it using Reinforcement Learning from Human Feedback (RLHF). Human raters score model responses, and the model learns to prefer answers that humans find helpful, honest, and harmless.

How it behaves: Natural conversation. It handles ambiguous questions, asks for clarification, and produces responses that feel helpful rather than mechanical. This is what makes ChatGPT feel different from a raw base model.

Examples: GPT-4o, Claude 3.5 Sonnet/Opus, Gemini 2.5.

Use cases: Chatbots, customer service, writing assistants, general-purpose AI assistants. This is the most widely deployed LLM type in consumer products.

Type 4: Reasoning Models

The newest type. These models use additional computation during inference (chain-of-thought reasoning, tree search) to "think through" complex problems before answering. They trade speed for accuracy on difficult tasks.

How it behaves: Slower than chat models, but significantly more accurate on math, logic, coding challenges, and multi-step reasoning. The model generates internal reasoning steps before producing a final answer.

Examples: OpenAI o1, OpenAI o3, DeepSeek-R1.

Use cases: Complex problem-solving (math competitions, scientific reasoning, legal analysis), tasks where accuracy matters more than speed, and research applications requiring reliable multi-step logic.

Type 5: Code Models

LLMs specifically trained or fine-tuned on programming languages and code repositories. They understand syntax, programming patterns, and can generate, debug, and explain code.

How it behaves: You describe what you want in natural language, and it writes code. Or you paste code, and it explains, debugs, or refactors it. Some can execute code and iterate based on results.

Examples: GitHub Copilot (powered by OpenAI Codex), Code Llama, DeepSeek Coder, StarCoder.

Use cases: Code generation, code review, debugging assistance, documentation writing, and automated testing. Integrated into IDEs as coding assistants.

Type 6: Multimodal Models

LLMs that accept and generate multiple types of content: text, images, audio, and sometimes video. They don't just process text. They can see, hear, and respond across formats.

How it behaves: You can send it a photo and ask questions about it. You can give it audio and get a text transcript. Some generate images or speech alongside text responses.

Examples: GPT-4o (text + image + audio input), Gemini 2.5 (natively multimodal), Claude 3.5 (text + image input).

Use cases: Visual question answering, document analysis (reading PDFs and images), voice assistants, and applications that need to process real-world inputs beyond text.

How These Types Relate

These six types aren't mutually exclusive. They build on each other in a progression.

A base model becomes instruction-tuned through fine-tuning. An instruction-tuned model becomes chat-aligned through RLHF. A chat-aligned model can become a reasoning model through additional training on reasoning data. Any of these can be extended to multimodal by adding vision or audio encoders.

Most production LLMs today are chat-aligned (Type 3) or reasoning models (Type 4) with multimodal capabilities (Type 6). The trend is toward models that combine all of these.

Quick-Reference Table

Type (What It Does / Example / Best For)

• Base - What It Does: Completes text - Example: Llama 3 base - Best For: Research, fine-tuning starting point
• Instruction-tuned - What It Does: Follows task instructions - Example: Llama 3 Instruct - Best For: Structured tasks (translation, extraction)
• Chat-aligned (RLHF) - What It Does: Natural conversation - Example: GPT-4o, Claude - Best For: Chatbots, assistants, writing
• Reasoning - What It Does: Thinks before answering - Example: o1, o3, DeepSeek-R1 - Best For: Math, logic, complex problem-solving
• Code - What It Does: Writes and debugs code - Example: Copilot, Code Llama - Best For: Development, code review
• Multimodal - What It Does: Handles text + images + audio - Example: GPT-4o, Gemini - Best For: Visual QA, document analysis, voice

Choosing the Right Type

Building a chatbot or assistant? Start with a chat-aligned model (Type 3). GPT-4o or Claude for closed-source simplicity. Llama 3 Instruct for open-weight flexibility.

Solving complex reasoning problems? Use a reasoning model (Type 4). Accept the slower speed for higher accuracy.

Building a coding tool? Use a code model (Type 5) or a general chat model with strong code capabilities (GPT-4o, Claude).

Processing images, documents, or audio? You need a multimodal model (Type 6).

Doing research or building a custom model? Start with a base model (Type 1) and fine-tune.

What Hardware These Models Need

LLMs are the most hardware-demanding AI model category. Model size determines what GPU you need.

Small models (7-8B parameters) fit on an L4 (24 GB) at INT4 quantization. Medium models (70B) require an H100 (80 GB) at FP8. Large models (405B+) need multiple GPUs with NVLink.

Per NVIDIA's H200 Product Brief (2024), the H200 delivers up to 1.9x inference speedup on Llama 2 70B vs. H100 (TensorRT-LLM, FP8, batch 64, 128/2048 tokens). The 141 GB VRAM accommodates 70B models at FP16 with headroom for concurrent users.

Getting Started

Pick the LLM type that matches your task from the table above. For closed-source models, call them through their provider's API. For open-weight models, you need GPU infrastructure to host them.

Cloud platforms like GMI Cloud offer GPU instances (H100 ~$2.10/GPU-hour, H200 ~$2.50/GPU-hour; check gmicloud.ai/pricing for current rates) for self-hosted LLM deployment, plus a model library for API-based access to image, video, and audio models.

Start with your task, pick the type, then choose the model.

FAQ

Which LLM type should beginners start with?

Chat-aligned models (Type 3) through an API. GPT-4o or Claude require no setup and respond naturally. Move to other types when your needs become more specific.

Can one model be multiple types?

Yes. GPT-4o is chat-aligned (Type 3), has strong code capabilities (Type 5), and is multimodal (Type 6). Modern frontier models increasingly combine multiple types.

Are reasoning models always better than chat models?

Not always. Reasoning models are better at complex logic but slower and more expensive. For simple conversation, a chat model is faster, cheaper, and equally effective.

What's the difference between instruction-tuned and chat-aligned?

Instruction-tuned models follow explicit instructions. Chat-aligned models go further: they handle ambiguity, maintain conversation context, and produce responses that feel naturally helpful. Chat-aligned models include instruction-following as a subset of their capabilities.

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