Large Action Models vs Large Language Models

Forget just chatting with AI, we’re now building AI that acts. Welcome to the age of Large Action Models.

You might know Large Language Models (LLMs) like GPT-4, Claude, Grok or Gemini for their uncanny ability to write, explain, summarise, and code. Impressive? Definitely. But Large Action Models (LAMs) take things further. These models don’t just understand language, they use it to get things done.

In this guide, we break down the key differences between LAMs and LLMs, when to use each one and real-world examples. We’re also going to look at why this shift matters for anyone building smarter AI agents, apps, or automation systems. 

So with that in mind, let’s get started!

What Are LLMs and LAMs?

Before we dive into comparisons, here’s a quick refresher on what each model does at its core:

What is a Large Language Model (LLM)? 

Large Language Models (LLMs) are foundational AI models trained on massive amounts of text data to understand, generate, and summarise human language. They power tools like ChatGPT, Gemini, and Claude, handling everything from creative writing to coding assistance.

Core strengths:

• Text generation and summarisation

• Translation and language understanding

• Content creation, dialogue, and Q&A

• Code completion and debugging

Examples: ChatGPT, Claude, LLaMA 3, Gemini

What is a Large Action Model (LAM)?

Large Action Models (LAMs) represent the next evolution in AI. They don’t just understand language, they execute goal-oriented tasks in response to it - combining natural language capabilities with symbolic reasoning, planning, tool use, and system integration to actually do things.

Core strengths:

• Multi-step task automation (e.g. “book a trip” or “process a refund”)

• Real-time interaction with external systems and APIs

• End-to-end workflow execution

• Autonomous planning and decision-making

Examples: Action Model, Rabbit R1, Auto-GPT-style agents are LAMs designed for agentic execution.

Now you understand what Large Language Models (LLMs) and Large Action Models (LAMs) are, let’s dive into their key differences. 

LAMs vs LLMs: A Side-by-Side Comparison

Key Differences of LAMs and LLMs

1. Capabilities

LLMs are brilliant at text. They can simulate conversations, draft essays, write code, or analyse sentiment - all within the bounds of a language interface.

LAMs, on the other hand, are doers. They can:

• Navigate websites

• Automate internal tools

• Send and respond to emails

• Coordinate across platforms (e.g. Notion → Slack → Airtable)

This leap in capability is what positions Large Action Models as the future! core infrastructure in next-gen AI systems and for AI agents.

2. Architecture

LLMs rely on transformer-based architectures trained on large text datasets. Their strength is in language pattern recognition, hence their name, large language models.

LAMs often layer:

• Language model input

• Planning module (symbolic or decision tree)

• Tool use (APIs, browser agents, command line)

• Memory and state tracking

This hybrid structure allows them to “think” in sequences, not just respond with paragraphs. A simple way to remember the difference? Large Language Models say, Large Action Models do.

3. Autonomy

While LLMs require constant prompting, LAMs can initiate actions without user nudging. Once triggered, a LAM can follow multi-step goals (e.g. “analyse my revenue data and prepare a client-ready summary and email it to me every month”) with little supervision.

But now, let’s explore some real world LAM and LLM examples…

Real-World Examples: LAMs vs LLMs in Action

LLM Example: Generating a Legal Contract Draft

You ask ChatGPT: “Draft a basic NDA between my company and a freelancer.”

It replies with a clear, legally formatted text. But that’s where it ends, you still have to:

• Email it

• Store it

• Monitor signatures

• Remind the recipient

LLM handles the content, but you remain the executor.

LAM Example: End-to-End NDA Handling

You ask a LAM-based AI agent the same: “Set up an NDA with our new freelancer.”

It then:

• Generates the contract

• Populates company details from your CRM

• Sends it via DocuSign

• Follows up via Slack

• Notifies you when it's signed

LAM handles the task, not just the content. This difference transforms productivity and user experience.

If you want to try a no-code LAM solution, then make sure to sign up to Action Model Waitlist today.

Use Cases Where LAMs Win

LAMs are designed for more operational, automated, and agentic roles. Here are some 2025-ready use cases:

Business Operations

• Automating onboarding workflows

• Triggering system-wide updates from voice or text commands

• Managing real-time dashboards based on changing inputs

Data & Reporting

• Cross-referencing CRM and financial data

• Scheduling weekly reports with dynamic updates

• Not just querying data, but acting on insights

Developer Tools

• Executing multi-step DevOps tasks (e.g. push code → trigger deployment → notify teams)

• Integrating with APIs or webhooks dynamically

Personal AI Agents

• Managing calendars, calls, and research tasks

• Acting on user intent across multiple tools (e.g. “Find time with Alice and book train tickets”)

Each of these examples highlights the critical difference in action orientation that separates LAMs from LLMs.

Challenges of LAMs (and the fixes How They’re Being Solved)

While LAMs are powerful, their deployment comes with hurdles:

1. Complexity of Execution

LAMs must handle errors, exceptions, and unexpected environments. This requires robust fallback systems, sandboxing, and tool integration layers.

2. Tool Access & Permissions

Secure access to APIs and internal systems introduces security, compliance, and infrastructure management challenges.

3. Evaluation Difficulty

Unlike LLMs (where fluency and accuracy are easier to test), LAMs require success metrics like task completion rates, action traceability, and real-world testing.

Current solutions include: 

Agent frameworks like LangGraph and AutoGen, which introduce planning modules, debugging tools, control flows, and modular LAM architecture.

Action Model, as an emerging LAM-first automation infrastructure, addresses deployment complexity by providing secure API integrations, error fallback systems, planning pipelines, and task traceability - reducing friction in enterprise use cases and enabling robust task execution.

Success isn't just fluency, but task completion, reliability, and latency. Tools like Action Model are building in traceability, testing harnesses, and observability.

Future Outlook: LAMs as the Foundation of Agentic AI

Large Action Models are paving the way for the next phase of intelligent automation: AI agents that can plan, reason, and act independently.

This is what we’re building with Action Model - the ability to create AI agents that don’t just produce text, but produce real actions. This is the next generation of true automation.

In 2025 and beyond, we’re seeing:

• Tool-using agents powered by LAMs becoming standard in enterprise systems.

• Dynamic agent swarms - LAM-powered bots collaborating across workflows.

• LAM + Web3 integrations, where autonomous agents operate on-chain tasks (DAO governance, DePINs, tokenomics).

As LAMs evolve, they’re expected to:

• Replace basic LLM-based assistants in complex work environments.

• Introduce agent reliability benchmarks (task success rate, action latency, etc.).

• Enable a new category of self-optimising software that adapts to goals, not just prompts.
  
  

Final Recap: LAMs vs LLMs

Large Language Models gave us conversational intelligence. Large Action Models deliver operational intelligence.

If LLMs helped us talk to machines, LAMs let machines work for us.

From automating workflows to powering autonomous agents, LAMs represent a major leap forward in the evolution of AI. As AI continues to decentralise, scale, and mature, LAMs will sit at the heart of next-gen productivity tools.  especially in Web3, enterprise, and agentic ecosystems.

Ready to build with Large Action Models?

The Action Model is pioneering agent-first infrastructure that turns LAM theory into real-world automation.

Join the waitlist to explore what LAM-powered agents can do for your business or Web3 system.