Understanding right LLM is crucial for building AI agents

Choosing the right LLM is crucial for building AI agents

But how to find which one to use? Let me break it down...

Language models are the brain powering next-generation AI agents, making it important to understand the popular types available.

📌 Today, let me break down the 6 popular LMs used in AI Agents:

1. GPT (General Pretrained Transformer)
- Example: GPT-3.5, Claude 3.5
- Predicts next words to generate fluent text, answer questions, chat, and complete tasks from prompts.

2. MoE (Mixture of Experts)
- Example: Deepseek V3, Mixtral 8x7B
- Activates select expert networks per input for efficient understanding and generation.

3. LRM (Large Reasoning Model)
- Example: Deepseek R1, OpenAI o1
- Performs multi-step reasoning and planning for more consistent, explainable AI responses.

4. VLM (Vision Language Model)
- Example: Qwen2.5-VL, GPT-4o (Vision)
- Understands images and text jointly to answer questions, describe, or generate multimodal content.

5. SLM (Small Language Model)
- Example: Google Gemma, Microsoft Phi
- Compact model for efficient, on-device language tasks like generation, summarization, or classification.

6. LAM (Large Action Model)
- Example: Salesforce xLAM, Rabbit AI’s R1
- Plans and executes structured actions or API calls from prompts for autonomous task completion.

7. HRM (Hierarchical Reasoning Model)
- Example: Sapient Intelligence HRM
- Uses a high-level planner and low-level executor to perform deep, structured reasoning without explicit chain-of-thought.

8. ToolFormer
- Example: Meta AI’s Toolformer
- Learns when and how to call external tools or APIs during generation using self-supervised training.

Now, which LM should you choose?

📌 Let me give you an overview:

1. GPT – Generate answers and call tools as per input
2. MoE – Handle high-volume requests with efficient resource usage
3. LRM – Complex problem-solving with multi-step reasoning
4. VLM – Computer-using AI agents and visual task automation
5. SLM – On-device agents with limited computational resources
6. LAM – Autonomous task execution and workflow automation
7. HRM – Goal-driven agents needing internal planning without verbose outputs
8. Toolformer – Agents that need to decide when and how to use external tools or APIs during generation