What are the Different Types of LLM Models?

Quick overview: 

• Model families: GPT, Gemini/PaLM, LLaMA, and Claude focus on text, coding, and reasoning. 
• Architectures: Transformer and Mixture of Experts designs affect scale and efficiency. 
• Training & capabilities: Data quality and alignment shape reasoning, safety, and performance. 
• Open vs proprietary: GPT, Gemini, and Claude are closed; LLaMA, Mistral, Grok, and DeepSeek are open or hybrid. 
• Emerging models: Mistral, Grok, and DeepSeek are gaining traction for strong benchmarks and efficient inference. 

In this guide, you’ll learn how major LLM families differ, what their architectures enable, how training choices affect performance, when to choose open or proprietary models, and which emerging options are worth exploring for stronger reasoning and generation capabilities. 

Lead the next wave of intelligent systems with upGrad’s Generative AI & Agentic AI courses or advance further with the Executive Post Graduate Certificate in Generative AI & Agentic AI from IIT Kharagpur to gain hands-on experience with AI systems.

Types of LLMs Based on Architecture 

This section explains how model design influences capabilities. You’ll see where decoder‑only, encoder‑decoder, and encoder‑only architectures fit, along with typical strengths, limits, and examples. 

Decoder‑Only Models (e.g., GPT, Llama, Falcon) 

Decoder-only models are autoregressive: they generate the next token based on previous tokens. This makes them highly effective for open-ended text generation, dialogue, code completion, and creative writing. 

Aspect	Summary
Traits	Left-to-right attention, strong text continuation
Strengths	Efficient long-form generation
Examples	GPT, LLaMA, Falcon, Mistral, Mixtral
Use cases	Chatbots, coding, content creation
Limitations	Limited bidirectional understanding

Encoder‑Decoder Models (e.g., T5, FLAN‑T5, BART) 

Encoder–decoder models combine strong text understanding with controlled text generation, making them ideal for tasks that require transforming or restructuring input content. 

Aspect	Details
Key traits	Bidirectional encoding with autoregressive decoding
Best at	Translation, summarization, style transfer, structured generation
Popular examples	T5, FLAN-T5, BART
When to use	Machine translation, abstractive summarization, data-to-text generation, information extraction with generation
Limitations	Less effective than decoder-only models for very long outputs or open-ended creative tasks

Encoder‑Only Models (e.g., BERT, RoBERTa) 

Encoder-only models focus on deep text understanding and representation learning, making them ideal for analysis and retrieval tasks rather than text generation.

Aspect	Details
Key traits	Bidirectional attention across the full input
Best at	Classification, NER, semantic embeddings, search and retrieval
Popular examples	BERT, RoBERTa, DeBERTa, MiniLM, DistilBERT
When to use	Intent detection, sentiment analysis, topic tagging, semantic search, RAG retrieval encoders
Limitations	Not suitable for generative tasks without added generation layers or models

Read about the Large Language Models: What They Are, Examples, and Open-Source Disadvantages 

Types of LLMs Based on Training Approach 

Here we contrast base models, instruction‑tuned models, and RLHF or preference‑optimized variants. The goal is to show how alignment choices change usefulness, safety, and task adherence. 

Base Models (Unsupervised Pretrained Models) 

Base models are trained at scale on diverse corpora using self-supervised objectives (e.g., next-token prediction or masked language modeling). They learn general linguistic and world knowledge but are not instruction-following by default. 

Use cases: 

• Foundation for downstream fine-tuning or prompting 
• Great starting point for domain adaptation (continued pretraining) 

Pros: 

• Broad generalization capabilities 
• Maximum flexibility for custom tasks 

Cons: 

• Raw base models may be verbose, unaligned, or less helpful for instructions until tuned. 

Instruction‑Tuned Models (e.g., FLAN, Llama‑2‑Chat) 

Instruction tuning exposes the model to curated prompt–response pairs across many tasks, making it follow instructions better, refuse unsafe requests, and respond concisely. 

Use cases: 

• General assistants, task-oriented chat, educational and productivity bots 

Pros: 

• More helpful, aligned responses 
• Better adherence to task constraints 

Cons: 

• Can be over-aligned or conservative 
• May inherit biases from instruction datasets 

Also Read: 23+ Top Applications of Generative AI Across Different Industries in 2025 

Reinforcement Learning Tuned Models (RLHF‑based) 

RLHF (Reinforcement Learning from Human Feedback) or similar preference optimization techniques further align models with human preferences for safety, style, and usefulness. 

Use cases: 

• Consumer-facing assistants requiring politeness, safety, and steerability 

Pros: 

• Higher-quality conversational behavior 
• Improved refusal handling and tone control 

Cons: 

• Potential reward hacking or over-optimization 
• May reduce diversity/creativity if not balanced with pretraining capabilities 

Must Read: LLM vs Generative AI: Differences, Architecture, and Use Cases 

Types of LLMs Based on Use Case 

This part maps model categories to real tasks. You’ll learn when to choose text generation, code‑specialized, or multimodal LLMs, and what quality signals to evaluate for each. 

Text-Generation LLMs 

These models optimize for natural language generation, reasoning, and multi-step instruction following. They can draft articles, emails, product descriptions, and summarize or explain complex topics. 

Typical tasks: 

• Summarization, rewriting, translation 
• Knowledge-grounded Q&A (often with RAG) 
• Brainstorming and ideation 

What to evaluate: 

• Hallucination rate, grounding support (RAG) 
• Long-context handling 
• Safety and controllability 

Code‑Generation LLMs (e.g., CodeLlama, StarCoder) 

Code LLMs are trained or specialized on code repositories and developer discussions. They understand libraries, common patterns, and natural language specs for programming tasks. 

Typical tasks: 

• Code completion, docstring generation 
• Bug fixing, explaining code, test generation 
• Multi-file refactoring with long-context windows 

What to evaluate: 

• Language coverage (Python, JS, Java, C/C++, SQL, etc.) 
• Security awareness, correctness, determinism 
• Tool use integration (linters, unit tests, agents) 

Multimodal LLMs (e.g., GPT‑4o, Gemini, Claude 3.5 Sonnet) 

Multimodal LLMs accept and reason over multiple input modalities, text, image, audio, and sometimes video, and produce text or other modality outputs. 

Typical tasks: 

• Image understanding and captioning 
• Chart/table interpretation 
• Visual question answering, document parsing 
• Audio transcription and speech-enabled agents 

What to evaluate: 

• OCR quality and diagram understanding 
• Safety on visual content 
• Latency for real-time interactions 

Emerging Categories of LLMs 

This section covers trends shaping deployments. It introduces small language models, open‑ vs closed‑source choices, and domain‑specific adaptations with their benefits and trade‑offs. 

Small Language Models (SLMs) 

SLMs target on-device or edge use with fewer parameters and optimized architectures. Despite being smaller, they can be surprisingly capable with strong data curation and distillation. 

Benefits: 

• Lower inference cost and latency 
• Privacy-preserving on-device usage 
• Suitable for offline or constrained environments 

Considerations: 

• Narrower knowledge breadth 
• More reliance on retrieval or tools to match large-model performance 

Open‑Source vs Closed‑Source LLMs 

Open-source models provide transparency, customization, and control over data and deployment, while closed-source offerings typically provide cutting-edge performance, managed infrastructure, and better support. 

Open-source pros: 

• Custom fine-tuning, self-hosting, compliance control 
• Cost control at scale 

Closed-source pros: 

• Strong benchmarks, frequent updates 
• Integrated tooling, guardrails, enterprise features 

Selection factors: 

• Compliance and data residency requirements 
• Total cost of ownership vs per-token pricing 
• Need for model customization vs ready-to-use performance 

Domain‑Specific LLMs (Healthcare, Legal, Finance) 

These models are adapted to specialized jargon, standards, and workflows. They can dramatically improve accuracy for domain tasks compared to general-purpose LLMs. 

Examples of tasks: 

• Healthcare: clinical note summarization, medical coding 
• Legal: contract analysis, clause extraction, drafting 
• Finance: earnings call analysis, risk summaries, KYC workflows 

Considerations: 

• Rigorous evaluation with domain metrics 
• Auditing, traceability, and human-in-the-loop review 
• Continual updates with fresh, compliant data 

Conclusion 

LLMs vary by architecture, training method, and use case. Newer developments like SLMs, domain‑specific tuning, and open‑ vs closed‑model choices make deployments more efficient. Pick an LLM by defining your task, checking cost, latency, and privacy needs, and benchmarking models with retrieval or fine‑tuning for reliability.