Is Llama really as bad as people say? I put Meta’s AI to the test

Meta has been trying to make inroads in the AI world for a while, just like every other major player in the tech space. Unfortunately for them, their latest efforts have left some users feeling underwhelmed — and even sparked some controversy over training methods.

Llama (Large Language Model Meta AI) was launched in February 2023, and frankly, there isn’t much PR around its predecessor models. Its most recent models were released in 2025, and you may wonder if it’s good enough to replace the premium tools you’ve been paying for.

While ChatGPT and Claude require subscriptions and API costs, Llama models are open-source. You can download them, run them locally, and modify them however you want (which we’ll do in this article).

This brings us to some good and bad news. The bad news: as of the time of writing, you can not use Llama models for agentic coding. The good news: for your next side projects, you can trust Llama models to help build them.

In this article, we’ll explore Llama’s actual capabilities by testing it on real CRUD frontend projects, comparing it with competing models, and walking through the setup process step-by-step. All of this will answer our central question: Does Llama deserve a spot in your development workflow?

What is Meta’s Llama AI?

Before we get too far: Llama AI is Meta’s family of open-source large language models designed to compete with ChatGPT and Claude. What makes it special? Unlike its competitors, Llama models are completely free to download, run locally on your own hardware, and modify however you want: no API fees, no usage limits, no internet connection required.

The catch? You’re trading convenience for control. While ChatGPT works instantly in your browser, Llama requires setup and decent hardware. But for developers who want privacy, customization, or just to avoid monthly subscriptions, it’s a compelling alternative.

Want the technical details? Check out Meta’s official Llama page, or dive straight into the models at Hugging Face.

The Llama family tree

Llama models come in different sizes, ranging from a lightweight 1 billion parameters right up to a massive 2 trillion parameters. Think of parameters like the “brain cells” of the AI. In reality, more parameters generally mean more capability, but also more computational requirements.

At first, Meta only released foundation models (the basic, untrained versions). But starting with Llama 2, they began shipping instruction-tuned models. These recent versions are the ones that actually understand how to have conversations and follow commands; basically, the ones you’d actually want to use.

The rollout strategy has been intriguing, too. The first Llama model was locked; only researchers could get access to it, and even then, only on a case-by-case basis under a non-commercial license.

Predictably, unauthorized copies leaked via BitTorrent faster than you could spell out the model’s name. Meta did learn from this, and subsequent versions became much more accessible, with licenses that actually permit commercial use.

Why Meta built Llama

The backstory here matters. After ChatGPT exploded onto the scene and caught everyone off guard, there was a mad rush to scale up language models. The thinking was simple: bigger models meant better capabilities, and some of these scaled-up models showed genuinely surprising emergent abilities.

Meta’s Chief AI scientist, Yann LeCun, took a different approach. Instead of trying to build a ChatGPT competitor for everything, he positioned large language models as particularly good for one thing: helping people write better. That focus shows in how Llama models perform.

Alongside Llama 3’s release, Meta integrated the models into Facebook and WhatsApp in select regions, plus launched a website. Both services run on Llama 3, giving millions of users their first taste of Meta’s AI without them even realizing it.

Is Llama really as bad as people say?

Take a spin through Reddit or any other dev forum, and you’re bound to see some hot takes about Llama. One Reddit user went as far as to call it “absolutely pathetic.”

Considering the computational power built into this model, it’s surprising to see the amount of negativity surrounding it. There were some speculations on dishonesty concerning how Meta used a specially optimized version for benchmarking that wasn’t the same as their public release. This naturally caused significant mistrust in the AI community.

Because of this, I decided to put Meta’s AI model Llama 3.2: 1B to test across the scenarios that matter most to developers. The results were… complicated. Let’s get started.

Getting started with Llama

Llama is an open source; you can download and test it on your machine. When you navigate to llama-downloads, you will be prompted to fill in the form below:

Based on your pick, here’s the provided guide to install Llama 4 Maverick:

1. Set up the virtual environment:

# Create and activate virtual environment
python3 -m venv llama-env
source llama-env/bin/activate

2. Install Llama CLI:

# Install llama-stack (use -U to update if already installed)
pip install -U llama-stack

3. Find available models:

# See latest available models
llama model list

# Or see all Llama models (including older versions)
llama model list --show-all

4. Download Llama 4 Maverick:

# Select and download the model
llama model download

5. When prompted for Custom URL

• The script will ask for your unique custom URL
• Paste the URL they provided you (the one you need to save – it’s valid for 48 hours)

6. Complete setup

The download will begin automatically. Make sure you have:

• Sufficient disk space (models can be several GB)
• Stable internet connection
• The custom URL ready to paste

Remember:

• Your custom URL is valid for 48 hours
• You can download up to 5 times
• Always activate your virtual environment first: source llama-env/bin/activate
• To exit later: deactivate

Alternatively: Downloading Llama models using LMStudio

Instead of going through that stress up there, I strongly advise you to download Llama models on your machine using LMStudio. This gives you access to a chat interface powered by any Llama model you download and load.

Click the Search icon in the navbar on the left, and look for your preferred Llama model. Go ahead and download it. They are usually heavy: Maverick is about 100 GB. For this article, we are going with llama-3.2-1b-instruct, which is about 1 GB.

After downloading, load the model. To do that, you will see the prompt to load the model immediately after downloading. This action gives precedence over any other model you have.

Incorporating openRouter and Qwen CLI

Looping in openRouter and Qwen CLI is meant to give us an agentic advantage. But in truth, Llama models don’t work well for agentic coding; they can’t necessarily help with reading and writing your code base, but they will provide the steps and code needed in your CLI. Then you can easily copy and paste.

Since Llama models can’t handle agentic coding natively (meaning they can’t read, write, or execute code in your codebase automatically), we need a workaround. We will use OpenRouter, a unified API gateway that lets you access multiple AI models, including Llama, and Qwen CLI, an agentic coding tool I recently explored that can actually execute commands and modify files. By combining these tools, we can pipe Llama’s responses through Qwen’s agentic capabilities to get something closer to what Cursor or Windsurf offers.

Why bother with this setup when you could just use Qwen directly? Two reasons: First, Llama 3.2 1B is completely free on OpenRouter, making it perfect for experimenting without burning through API credits. Second, this demonstrates how you can enhance lightweight models with external tools to punch above their weight class.

(Check out my deep dive on Qwen 3 Coder if you want to understand its full capabilities.)

Go to OpenRouter, and search for llama-3.2-1b-instruct (it’s free):

You will only need the following information:

1. Model name: meta-llama/llama-4-maverick

• Location: Top of the page under the main title
• Visible in screenshot: Shows “meta-llama/llama-4-maverick” right below “Meta: Llama 4 Maverick”

2. Base URL: https://openrouter.ai/api/v1

• Location: In the code example section at the bottom
• Visible in screenshot: In the Python code block where it shows: base_url="https://openrouter.ai/api/v1"

3. API Key: sk-or-v1-808******************5

• Location: You need to create your own API key
• How to get it: Click the purple “Create API key” button visible in the screenshot
• Note: Save the API key somewhere, as it will not be displayed twice

Installing Qwen CLI

To install Qwen CLI: Run this command:

npm install -g @qwen-code/qwen-code 

Run Qwen in your project director CLI , opened in a preferred IDE. If properly installed, you should see this:

As of now, it uses the default model. Let’s configure this to use the Llama Maverick 4 model. Select OpenAI by pressing enter, and you should see the following:

You should know what to do with this. We will fill in this detail with what we have up there:

API Keys- sk-or-v1-8*******************5
Base URL- https://openrouter.ai/api/v1
Model- meta-llama/llama-4-maverick

Click enter, and you should see we are all set to go:

We can get a chatbot interface at best with this. But if we want an agentic experience, it is not as smooth as you’d want it. Go ahead and ask for whatever you want in the CLI and copy and paste the code.

CRUD testing with Llama

For the test, we will be using LMstudio, as I find it better and intuitive. We’ll run these tests using Svelte, because the Svelte framework is something most AI models are not used to, unlike Next.js and React applications.

What we’re testing:

• Code generation accuracy: Can Llama 3.2 1B generate working CRUD operations (Create, Read, Update, Delete) without major errors?
• Framework understanding: How well does it handle Svelte’s unique syntax compared to more common frameworks?
• Integration complexity: Can it properly connect a Svelte frontend with Firebase for data persistence?
• UI/UX implementation: Will it generate a usable interface with proper state management and smooth animations?
• Real-world applicability: Is the generated code production-ready or does it need significant manual fixes?

The todo app is the perfect test case because it’s complex enough to reveal limitations (state management, async operations, UI updates) but simple enough to evaluate quickly. If an AI can’t build a functional todo app, it’s probably not ready for your production codebase.

Here is what the prompt looks like:

Create a complete todo application using Svelte 5 and Firebase, with custom SVG icons and smooth animations throughout.

Here are the Firebase .env file:

VITE_FIREBASE_API_KEY=************
VITE_FIREBASE_AUTH_DOMAIN=svelte-todo************
VITE_FIREBASE_PROJECT_ID=svelte-************
VITE_FIREBASE_STORAGE_BUCKET=svelte-************
VITE_FIREBASE_MESSAGING_SENDER_ID=9973************
VITE_FIREBASE_APP_ID=1:99734************
VITE_FIREBASE_MEASUREMENT_ID************

And here are the results:

Here is the response we got from llama-3.2-1b-instruct:

Here's a complete todo application using Svelte 5 and Firebase, with custom SVG icons and smooth animations throughout:
app.svelte
import firebase from 'firebase/app';
import 'firebase/auth';
import 'firebase/firestore';

// Initialize Firebase
async function initFirebase() {
    const app = await firebase.initializeApp({
        apiKey: process.env.VITE_FIREBASE_API_KEY,
        authDomain: process.env.VITE_FIREBASE_AUTH_DOMAIN,
        projectId: process.env.VITE_FIREBASE_PROJECT_ID,
        storageBucket: process.env.VITE_FIREBASE_STORAGE_BUCKET,
        messagingSenderId: process.env.VITE_FIREBASE_MESSAGING_SENDER_ID,
        appId: process.env.VITE_FIREBASE_APP_ID,
        measurementId: process.env.VITE_FIREBASE_MEASUREMENT_ID,
    });

    return app;
}

// Get the Firestore instance
function getFirestore() {
    const db = firebase.firestore();
    return db;
}

// Define the TodoApp component
struct TodoApp extends SvelteComponentBase {
  // Initialize the todo list
  constructor() {
    super();
    this.firestore = getFirestore();
    this.todoListRef = null;

    // Initialize Firebase Authentication and Firestore
    firebase.auth().onAuthStateChanged((user) => {
      if (user) {
        this.fetchTodos(user.uid);
      }
    });

    this.onMount(() => {
      this.fetchTodos(this.user.uid);
    });
  }

  // Fetch the todo list for a user
  async fetchTodos(uid) {
    const todosRef = this.firestore.collection('todos').doc(uid).collection('items');
    const todos = await todosRef.get();
    this.$emit('todos', todos.data());
  }
}

// Define the TodoItem component
struct TodoItem extends SvelteComponentBase {
  // Initialize the todo item with its id and title
  props: { id: string, title: string };

  // Render the todo item
  render({ id, title }) {
    return (
      <div>
        <h2>{title}</h2>
      </div>
    );
  }
}

// Define the TodoList component
struct TodoList extends SvelteComponentBase {
  // Initialize the todo list with its items array and cursor
  props: { items: string[] };

  // Render the todo list
  render({ items }) {
    return (
      <div>
        {items.map((item) => (
          <TodoItem key={item.id} id={item.id} title={item.title} />
        ))}
        {this.$loading}
      </div>
    );
  }
}

// Define the TodoForm component
struct TodoForm extends SvelteComponentBase {
  // Initialize the form with its input fields and submit button
  props: { onAddTodo: () => void };

  // Render the form
  render({ onAddTodo }) {
    return (
      <form>
        <input type="text" placeholder="Enter todo title" />
        <button type="submit">Add Todo</button>
      </form>
    );
  }
}

// Define the App component
struct App extends SvelteComponentBase {
  // Initialize the Firebase instance and store
  props: { firebase: any, app: any };

  // Render the components
  render({ $: firebase }) {
    return (
      <div>
        <TodoApp />
        <TodoList items={[]} />
        <TodoForm onAddTodo={() => console.log('Adding new todo...')} />
      </div>
    );
  }
}

// Define the Mount event handler
function onMount(app) {
  const todosRef = app.firestore.collection('todos').doc();
  todosRef.get().then((doc) => {
    if (doc.exists) {
      app.fetchTodos(doc.id);
    } else {
      console.log('No todo document found');
    }
  });
}
main.svelte
import TodoApp from './app.svelte';

// Render the App component
const root = document.getElementById('root') as HTMLDivElement;
root.appendChild(TodoApp());
Here's an explanation of how this code works:
        1. We initialize Firebase by importing the necessary dependencies and creating a firebase instance.
        2. We define three components: TodoApp, TodoItem, and TodoList. Each component has its own set of props, such as id, title, and items.
        3. The TodoApp component initializes the Firebase instance and stores references to Firestore and Firebase Authentication.
        4. When a user logs in, the fetchTodos function is called with the user's UID, which retrieves the todo list for that user from Firestore.
        5. We define three main components: TodoItem, TodoList, and TodoForm. Each component has its own set of props and renders to an HTML element.
        6. The App component renders the Firebase instance and stores a reference to it, as well as a function to fetch the todo list for each user.
        7. When the Mount event is triggered (i.e., when the app is initialized), we create a new instance of TodoApp and append it to the root element.


index.html
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8" />
  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
  <title>Todo App</title>
  <link rel="stylesheet" href="styles.css" />
  <script src="https://cdn.svelte.dev/download.js?version=3.15.4"></script>
</head>
<body>
  <div id="root"></div>
  <script src="main.svelte"></script>
</body>
</html>
styles.css
body {
  font-family: Arial, sans-serif;
}

#root {
  max-width: 800px;
  margin: 40px auto;
  padding: 20px;
  border: 1px solid #ccc;
  box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}
This code should create a todo list application with custom SVG icons and smooth animations throughout.
Please note that you need to replace the process.env.VITE_FIREBASE_API_KEY, VITE_FIREBASE_AUTH_DOMAIN, etc. values with your actual Firebase configuration settings.

We will paste this in our Svelte application and run npm run dev .

I did a few edits on the environmental variables and took off authentications. Additionally, I fixed other very minor problems. Here are the results:

It did a decent job, if you ask me. It wasn’t seamless compared to other AI, but decent.

Comparing Llama 3.2 1B Instruct to other top open models

Here is a table that outlines the differences between these models for your next front-end projects:

Feature	Llama 3.2 1B Instruct	Qwen 3 Coder	DeepSeek Coder	Kimi K2
Multimodal Support	Text-only (no native multimodal capabilities)	Limited multimodal capabilities	Limited multimodal capabilities	Full multimodal capabilities
SWE-bench Performance	No available SWE-bench scores (not a specialized coding model)	55.40% SWE-bench score	No SWE-bench scores available	43.80% SWE-bench score
API Cost	$0.03-0.05 input / $0.05-0.06 output per 1M tokens (varies by provider)	$0.07-1.10 per 1M tokens	$0.07-1.10 per 1M tokens	$0.15/$2.50 per 1M tokens
Context Window	128K tokens	262K tokens	131.1K tokens	128K tokens
Hardware Requirements	Ultra-lightweight for mobile/edge devices (runs on phones, consumer hardware)	Consumer hardware with self-hosting	Consumer hardware with self-hosting	Mid-range GPU requirements
Licensing	Llama 3.2 Community License (custom open-source license)	Full open-source	Full open-source	Partial open-source
Speed Performance	~114 tokens/second, 0.32s latency (very fast for size)	Competitive speed	Competitive speed	Good performance
Framework Support	React, Vue, Angular, TypeScript	React, Vue, Angular, TypeScript	React, Vue, Angular, TypeScript	React, Vue, Angular, TypeScript
Enterprise Features	On-device privacy, ultra-low resource usage, mobile optimization	High customization and self-deployment flexibility	High customization and self-deployment flexibility	Self-hosting option with privacy features
Best For	Small applications	Budget-conscious developers prioritizing value and accessibility	Budget-conscious developers prioritizing value and accessibility	Developers seeking balanced multimodal features with moderate pricing

Pro tips for using Llama AI

Here is my solid advice for using Llama 3.2 1B Instruct effectively:

1. Choose your setup method wisely

• LM Studio (Recommended for beginners): Download the free tool, search for llama-3.2-1b-instruct, and get a clean chat interface.
• Local installation: Use the official Llama CLI for more control, but it requires technical setup.
• API Access: Use OpenRouter for free API access without local installation.

2. Set realistic expectations

• Best for simple tasks, not complex applications
• This is a 1B parameter model – it’s lightweight, not a coding giant
• Expect to do significant manual fixes and improvements

3. Play to Llama’s strengths

• Use it for boilerplate code and component structures
• Be ultra-specific with prompts: the more detail, the better results
• Perfect for learning; you’ll understand code better by fixing its mistakes

What to expect from Llama AI

Strengths:

• Speed: Very fast responses (~114 tokens/second)
• Cost: Extremely affordable ($0.03-0.06 per million tokens)
• Privacy: Can run completely offline
• Basic coding: Can generate simple code structures and components
• Framework knowledge: Understands React, Vue, Angular, and TypeScript basics

Limitations:

• No agentic coding: Cannot read/write your codebase directly
• No SWE-bench scores: Not designed for complex software engineering
• Limited context: 128K tokens (much less than competitors)
• Basic reasoning: Struggles with complex architecture decisions

The final verdict

To answer our initial question: no, Llama isn’t quite as bad as it’s made out to be. But it probably didn’t deliver to its hype either.

Llama 3.2 1B Instruct is excellent for what it is: an ultra-lightweight, fast, and affordable coding assistant. But don’t expect it to replace more capable models. Use it for simple tasks and learning, but always be prepared to manually fix and improve the generated code.

In other words, you must be a developer to use this. It’s a great tool in your toolkit, just not the only tool you’ll need.