As a developer, having an AI-powered coding assistant can supercharge your workflow, from writing cleaner code to debugging complex issues. While proprietary tools like GitHub Copilot are popular, open-source large language models (LLMs) offer powerful, customizable, and cost-effective alternatives. In 2025, the open-source ecosystem is thriving with models fine-tuned for coding tasks, supporting everything from Python to niche languages. In this blog, we’ll explore the top open-source LLMs for coding, their strengths, and how you can leverage them to boost your productivity.

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Top Open-Source LLMs for Coding

1. CodeLlama (Meta AI)

• Parameters: 7B, 13B, 34B, 70B
• What It Does: Built on Llama 2, CodeLlama is fine-tuned for code generation and understanding across languages like Python, C++, and Java. Variants like CodeLlama-Instruct excel at following natural language prompts, while CodeLlama-Python is a Python specialist.
• Performance: Scores 53.7% on HumanEval (34B) and 56.2% on MBPP, making it a strong contender for general coding tasks.
• License: Llama 2 community license (research-focused, some commercial restrictions).
• Best For: Developers needing robust code generation and debugging.
• Get It: Available on Hugging Face and Ollama.

2. Qwen2.5-Coder (Alibaba Cloud)

• Parameters: 7B (Instruct)
• What It Does: A versatile model supporting 92 programming languages, with a massive 128K token context window for handling large codebases. It’s great for code generation, summarization, and bug fixing.
• Performance: Matches GPT-4o on coding benchmarks, especially for multi-language tasks.
• License: Apache 2.0 (commercial-friendly).
• Best For: Multi-language projects and enterprise-grade workflows.
• Get It: Hugging Face, vLLM, or other frameworks.

3. StarCoder2 (BigCode)

• Parameters: 15B
• What It Does: Trained on The Stack v2 (3B+ files, 600+ languages), StarCoder2 shines in code completion and contextual understanding. Its fill-in-the-middle (FIM) capability is perfect for IDE integrations.
• Performance: Excels in Python, JavaScript, and TypeScript, with high accuracy in autocompletion.
• License: Permissive open-source license (commercial-friendly).
• Best For: Enterprise developers and IDE autocomplete.
• Get It: Ollama, Hugging Face.

4. DeepSeek V3

• Parameters: 671B
• What It Does: A heavyweight model with a 128K token context window, ideal for large-scale coding tasks like enterprise software development and bug detection.
• Performance: Rivals GPT-4o on coding benchmarks, with top-tier accuracy.
• License: MIT (commercial-friendly, some restrictions).
• Best For: Teams with access to high-end hardware (e.g., H100 GPUs).
• Get It: Hugging Face, serverless platforms like Koyeb.

5. Granite Code Models (IBM)

• Parameters: 3B, 8B, 20B, 34B
• What It Does: Designed for enterprise software development, Granite supports 116 programming languages and tasks like code generation, fixing, and explanation. It’s optimized for privacy and local deployment.
• Performance: Outperforms Mistral-7B on HumanEvalPack and MBPP benchmarks.
• License: Apache 2.0 (commercial-friendly).
• Best For: Application modernization and on-device coding.
• Get It: Hugging Face, Ollama.

6. Mistral 7B / Mixtral 8x7B (Mistral AI)

• Parameters: 7B (Mistral), 46.7B total but 12.9B active (Mixtral)
• What It Does: Mistral 7B is a lightweight coding powerhouse, while Mixtral 8x7B (a Mixture of Experts model) offers efficiency for complex tasks. Both handle multiple languages well.
• Performance: Competitive with Llama 2 70B on coding tasks.
• License: Apache 2.0 (permissive).
• Best For: General coding and resource-efficient environments.
• Get It: Hugging Face, Perplexity Labs API.

7. Phi-4 (Microsoft)

• Parameters: 3.8B (Phi-3 Mini)
• What It Does: A compact, instruct-tuned model optimized for coding and reasoning. It runs smoothly on consumer hardware, making it accessible for solo developers.
• Performance: Outperforms larger models on some benchmarks, ideal for edge computing.
• License: Permissive open-source license.
• Best For: IDE autocomplete and mobile/edge apps.
• Get It: Hugging Face, Koyeb.

🚀 Supercharge Your Development Workflow with AI

Open-source LLMs are redefining how we code—giving developers the freedom, power, and flexibility to build smarter, faster, and more securely. Whether you’re crafting complex enterprise applications or working on solo passion projects, there’s an open-source model ready to support you.

👉 Want to see AI in action?
Check out how we used open-source tools to build an AI-powered app in our blog:
🔗 Building a Thirukkural-Finding AI Bot with Flask and Python

🎯 And if you’re looking to land your next role using AI:
🧠 Turbocharge Your Job Search: 10 AI Tools to Land a Job in Just 7 Days