6 Best LLMs for Coding: Copilot, Llama 3, and More

We’ve all been there: you’ve written a function to validate an email address, but the regex is throwing an error. Instead of burning the next hour on Stack Overflow, you ask an AI assistant to fix it. Seconds later, you have a corrected, optimized code snippet. This is the new reality for developers using Large Language Models (LLMs) trained for coding. These tools aren’t just for boilerplate anymore; they are becoming true partners in the development lifecycle, capable of debugging, refactoring, and even architecting complex solutions.

But how do you choose the right one? A solo developer working on open-source projects has completely different needs than an enterprise team handling proprietary software. So, I’ll break down the top LLMs for coding, looking at their strengths, weaknesses, and who they’re really for. The goal is to help you find the perfect coding assistant for your workflow.

GitHub Copilot: The Integrated Enterprise Standard

GitHub Copilot is probably the most well-known code completion and generation resource out there, developed by GitHub and OpenAI. Think of it as an AI pair programmer that lives directly inside your Integrated Development Environment (IDE), offering real-time suggestions as you type. Since it’s built on OpenAI’s powerful GPT-4 model, it has been trained on a massive amount of public code, mostly from GitHub itself.

Its biggest selling point, in my opinion, is the integration. Copilot has first-party extensions for popular IDEs like Visual Studio Code, the JetBrains suite (IntelliJ, PyCharm), and Neovim. It’s a . This tight integration allows the model to analyze the context of your entire project—including open files and existing code—to provide highly relevant suggestions. For instance, imagine you start writing a function that interacts with another class in your project. Copilot gets it. It understands the methods and properties of that class and suggests code that fits your existing patterns.

On top of that, Copilot offers enterprise-grade features for businesses. Administrators can manage policies, plus the service promises that your code snippets won’t be used to train public models. While it does require a subscription ($10/month for individuals, with higher tiers for businesses), the unlimited interactions and deep IDE integration make it a top choice for professional teams who want to boost productivity without the headache of self-hosting.

Open-Source Models: Control, Privacy, and Customization

What if you don’t want your code sent to a third-party server? That’s where open-source coding LLMs come in. These are models whose architecture and weights are public, letting anyone download, modify, and run them on their own hardware. This approach gives you complete control over your data—a critical factor when you’re working with sensitive or proprietary code. Unlike cloud-based services, a locally hosted model keeps everything on your machine. Simple as that.

Two of the biggest players in this space are Alibaba’s Qwen and Meta’s Llama series. A huge plus is that these models can be fine-tuned on your company’s private codebases. Imagine creating a specialized assistant that already knows your internal libraries, coding standards, and architectural patterns. The trade-off, however, is the hardware. Running these models effectively requires a beefy GPU with significant VRAM—usually 16GB or more for smaller models and way more for the big ones. Besides the hardware cost, you’re also on the hook for setup and maintenance.

Qwen: The Specialist Built for Code

Qwen is an open-source LLM from Alibaba’s research division, with versions built specifically for coding tasks. Here’s the key difference: unlike general-purpose models that just happen to be good at programming, Qwen was trained from the ground up on trillions of tokens of code-related data. It supports over 90 programming languages. This specialized training gives it a serious edge in understanding syntax, libraries, and common programming idioms.

The Qwen-Code model, especially the 7-billion-parameter version (Qwen-7B-Code), really hits a sweet spot between performance and accessibility. It holds its own against much larger models, yet it’s small enough to run on a consumer-grade GPU with at least 16GB of VRAM. This makes it a practical choice for individual developers or small teams who want the benefits of a self-hosted solution without dropping cash on an enterprise-level server rack. Its performance in benchmarks often surpasses older models like GPT-3.5, providing a strong, free alternative to subscription services.

Meta Llama 3: The Generalist That Excels at Coding

Meta’s Llama 3 is an open-source, general-purpose LLM that’s proven to be surprisingly good at code generation and reasoning. What’s interesting is that the generalist Llama 3 model often outperforms Meta’s own specialized Code Llama model in coding benchmarks. It seems a strong foundation in general reasoning and language understanding can be more powerful than narrow, code-only training. Who would’ve thought?

Llama 3’s real strength is its versatility. For example, you can use it to draft an email, summarize a technical document, and then write a complete Python script for a chess game—all within the same conversation. For developers, this means having a single resource for a wide range of tasks. The smaller Llama 3 models can be hosted locally, while the larger, more powerful versions are accessible through cloud providers like AWS and Azure on a pay-per-token basis. This flexibility lets you scale your usage based on your budget and hardware. Since it’s a general-purpose solution, investing time to learn its nuances can pay dividends across many areas, a concept you can explore further in some of the best free AI courses available.

Anthropic’s Claude 3: For Complex Code Generation and Analysis

Claude 3 is a family of models from Anthropic known for two things: a massive context window and strong performance in complex reasoning. What does a large context window mean for you? It means the model can process and remember a huge amount of information at once—up to 200,000 tokens, which is like feeding it a whole book. For developers, this means you can give the model an entire codebase or multiple large files for analysis, debugging, or refactoring.

Where Claude 3 really shines, in my experience, is in generating and explaining large, intricate blocks of code. While other models are great at writing small functions, you can ask Claude to design a multi-file application, and it can actually provide a coherent, well-structured solution. Plus, it’s excellent at explaining its own code or deciphering that legacy codebase you’re struggling to understand. Even though it’s a proprietary, API-only model, its superior ability to handle complexity makes it my go-to choice for challenging, large-scale programming tasks.

How to Choose the Right Coding LLM for You

So, how do you pick the right AI coding assistant? It comes down to balancing a few key factors based on your specific situation.

• Integration and Workflow: If you want a tool that works out-of-the-box inside your IDE with zero setup, GitHub Copilot is the clear winner. That integration is its biggest selling point.
• Privacy and Control: For anyone working with proprietary code or who prioritizes data privacy, a self-hosted open-source model like Qwen or Llama 3 is the only truly safe bet. This keeps all your data on your own hardware. Still, you should remain aware of security risks, as detailed in cases like the Amazon Q security breach and prompt injection risks.
• Task Specificity: If your main goal is raw code generation and you need a highly specialized model, Qwen is an excellent choice. But for more complex, architectural tasks and deep code analysis, Claude 3’s huge context window is a major advantage.
• Cost and Scalability: For individuals, a $10/month Copilot subscription is pretty straightforward. For experimentation, running a small Llama 3 model locally has no recurring cost beyond the initial hardware investment. Meanwhile, for businesses, the per-user fees of Copilot or the pay-per-token APIs for Claude and Llama 3 need to be weighed against project budgets.

Look, the best way to figure out which LLM fits your coding style is to just try one. It’s that simple. Sign up for the 30-day free trial of GitHub Copilot to experience a fully integrated assistant. If you have a capable GPU, download a small open-source model like Qwen-7B-Code or Llama-3-8B and see how a self-hosted solution feels. I’ll be honest, by spending just a few hours with each, you will quickly discover which tool complements your workflow and genuinely makes you a more efficient developer.