[June 2025] Best LLMs for Coding Ranked: Free, Local, Open Models

If you're trying to pick the best LLM for coding in 2025, we got you covered. Nut Studio Team spent weeks testing the top models—both local and online—to see how they really perform.

Whether you care about raw speed, full-project context, or models that run on a budget GPU, this guide will help. Let's break it all down—so you can pick the best model for code that actually fits your workflow.

First things first—if you're looking for the best coding LLM, here's what really matters, especially if you're just getting started.

Not all AI models are built the same. Some are trained to write simple scripts, while others can handle full projects, explain their steps, and even debug real software issues. To figure out which one fits your needs, we look at two things: benchmarks and core metrics.

• HumanEval & MBPP test if the model writes correct Python functions. Top models now score over 90% on HumanEval Pass@1, thanks to better prompt design and fine-tuning. MBPP checks basic Python skills.
• SWE-Bench & LiveCodeBench use real GitHub issues to test how well models handle real-world coding. These benchmarks go beyond snippets to assess bug fixing and code editing abilities.
• BigCodeBench focuses on complex tasks like generating multiple function calls and managing logic flow, testing a model's reasoning power.
• Spider 2.0 checks SQL skills. It measures how well a model understands databases, schemas, and writes complex SQL queries.
• For Python coding, choose models with high HumanEval or MBPP scores. For bug fixing, go with SWE-Bench or LiveCodeBench leaders. For SQL tasks, pick those that excel on Spider 2.0.

Benchmarks are helpful, but they're only part of the story. To pick the best LLM model for coding, especially if you're new to AI tools, here are the core features you should care about:

Benchmarks help—but they don't tell the whole story. A model that scores well might still be hard to install, slow on your hardware, or tricky to use offline. That's why in the next section, we'll explore how open-source models stack up against the big closed ones—and which gives you more freedom.

When picking the best LLM model for coding, one big choice is whether to use an open-source model or a closed-source one. Both have pros and cons—and the right choice depends on what matters most to you.

Closed-source models like GPT-4, Claude 3.5, or Gemini are powerful. They're great at code generation, often lead benchmark scores, and are easy to use with tools like GitHub Copilot. But they run in the cloud. That means you need internet access, and your code is shared with external servers. This may raise privacy or cost concerns.

Open-source models, like Code LLaMA 3, StarCoder2, or DeepSeek-Coder are free to use and can run entirely on your device. They give you full control. You can tweak them, run them offline, and avoid sending code to the cloud. That’s a big plus if you care about data privacy, offline coding, or building your own AI tools.

Here's a quick comparison:

When it comes to cloud-based AI models for code generation, a few clear leaders stand out in 2025. From my hands-on experience and community feedback, Claude 3.5 Sonnet and GPT-4.5 are top choices for developers working on real projects—whether frontend, backend, full-stack, or scripting tasks.

Claude 3.5 Sonnet shines in reasoning and creating well-structured code, making it ideal for debugging and generating complex algorithms. Meanwhile, GPT-4.5 offers strong natural language understanding, making it easy to work with and great for integration with popular tools like GitHub Copilot.

Here's a side-by-side look at their performance based on key coding benchmarks:

Benchmarks like HumanEval test a model's ability to write correct Python functions. MBPP focuses on basic Python tasks, while DS-1000 reflects how well a model handles diverse coding challenges. These numbers come from recent academic and community benchmarks, as well as developer feedback from Reddit and GitHub discussions.

In practice, GPT-4.5's speed and seamless IDE plugins make it very popular for full-stack coding, while Claude 3.5's logic strength is favored for backend debugging and algorithm-heavy tasks.

If you want the best of both worlds with local control and no coding hassle, Nut Studio lets you download and run some of these powerful models offline. That way, you can enjoy privacy and speed without depending on the cloud.

More developers now prefer local LLMs for coding because of privacy, cost savings, and offline use. Running AI on your own PC keeps your code private and avoids cloud fees. Here are the best open source LLM for coding in 2025:

These models benefit from advanced quantization techniques like BitsandBytes, GGUF, and GPTQ, which help them run efficiently even on mid-range GPUs.

If setting up local LLMs sounds complicated, Nut Studio makes it simple. It's a free desktop app that lets you download and run local coding models with just one click—no terminal or coding skills needed.

Nut Studio automatically detects your hardware and picks the best compatible model, so you get the fastest, smoothest experience without any setup stress. Whether you want to try Code Llama, DeepSeek R1,or Mistral, Nut Studio handles all the installation and configuration behind the scenes. This is the easiest way to start coding offline and keep your data private.

Key Features:

• Download and launch 20+ top LLMs like Llama, Mistral, Gemma.
• Easy setup with no coding, perfect for beginners and pros.
• No internet required. Use local LLMs for coding anytime, anywhere, completely offline.
• Your data stays on your device. Nothing is uploaded or tracked.

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1 What is the best LLM for coding right now?

The best LLM for coding right now depends on your needs. For cloud use, GPT-4.5 and Claude 3.5 Sonnet lead the pack. For local setups, models like Code Llama 3 and DeepSeek-Coder stand out. These models score high on benchmarks like HumanEval and offer great real-world coding help.

2 Which AI model performs best for real-world coding tasks?

Models like GPT-4.5 excel at handling complex projects, debugging, and multi-language support. Locally, DeepSeek-Coder and Code Llama 3 are strong performers that you can run without internet.

3 Can I run a coding LLM locally for free?

Yes. Many open-source coding LLMs like StarCoder2 and DeepSeek-Coder are free to download and run on your own PC. You just need compatible hardware and the right tools. With Nut Studio, you don't need to write any terminal commands — just download, click, and run.

Nut Studio

With Nut Studio, you don't need to write any terminal commands — just download, click, and run.

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4 What's the best Ollama model for coding?

The best Ollama model for coding right now is Code Llama 3. It balances performance and usability well, and Ollama supports running it locally with easy commands.

5 Do I need a GPU to use a local coding LLM?

You usually need a decent GPU with enough VRAM (8GB or more) for smooth local coding LLM performance. Some smaller models run on CPU but will be slower. Nut Studio checks your system and recommends the best local model for code — no hardware guesswork.

6 Is it safe to run these models offline?

Absolutely. Running LLMs locally means your code stays on your device. No data is sent to external servers, which keeps your projects private and secure.

Nut Studio

Nut Studio checks your system and recommends the best local model for code — no hardware guesswork.

Free Download

If you're looking for the best AI models for coding, your choice depends on whether you prefer speed and convenience from cloud tools or full privacy and control with local setups. For developers who want to work offline, avoid cloud costs, and keep their code private, local models like DeepSeek-Coder and Code Llama 3 are top picks.

Nut Studio makes it easier than ever to try the best local LLM for coding—with no coding needed. Just install, click, and start building with AI, right on your own device.

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Article by

Aaron Smith

Aaron brings over a decade of experience in crafting SEO-optimized content for tech-focused audiences. At Nut Studio, he leads the blog’s content strategy and focuses on the evolving intersection of AI and content creation. His work dives deep into topics like large language models (LLMs) and AI deployment frameworks, turning complex innovations into clear, actionable guides that resonate with both beginners and experts.