Top Open-Source Models for Code Generation in 2025

AI-driven code generation has transformed the way developers write, debug, and optimize software. With open-source models becoming more powerful and accessible, developers no longer have to rely on proprietary solutions like GitHub Copilot or OpenAI’s GPT-4.

In this article, we’ll explore Top 5 Open-Source Coding Models with less than 7 billion parameters that have demonstrated strong performance in code generation, reasoning, and completion. These models are ideal for developers who want AI-assisted coding without relying on proprietary solutions like GitHub Copilot or OpenAI’s GPT-4.

We’ll compare these models based on their capabilities, training approach, and practical applications, using HumanEval scores as a performance benchmark.

Lets learn about HumanEval

When evaluating AI models for code generation, one of the most reliable metrics is HumanEval—a benchmark created by OpenAI that tests how well a model can generate functionally correct code. It consists of 164 programming problems that require a model to complete partially written functions and pass corresponding unit tests.

HumanEval is an excellent way to measure the accuracy, coherence, and problem-solving ability of AI coding models, as it assesses their capability in understanding logic, syntax, and best coding practices. A higher HumanEval score means the model is more reliable for software development tasks.

Lets dive in and understand which model performs best on HumanEval benchmarks:

1) Qwen2.5-Coder-7B-Instruct

The Qwen2.5-Coder-7B-Instruct is a specialised large language model developed by Alibaba Cloud's Qwen team, tailored for code-related tasks. As part of the Qwen2.5-Coder series, it is designed to assist developers in code generation, reasoning, and debugging across multiple programming languages.

HumanEval Score: 88.4%

Key Features:

• Instruction Tuning: Fine-tuned to follow prompts accurately for precise code generation.
• Long-Context Support: Handles up to 128,000 tokens, making it effective for large codebases.
• Multi-Language Support: Proficient in 92 programming languages, including Python, Java, and C++.
• Extensive Training Data: Trained on 5.5 trillion tokens for superior code reasoning and accuracy.

Conversations around the Model:

Reddit Post, Twitter Post

2) WaveCoder-Ultra-6.7B

The WaveCoder-Ultra-6.7B is a specialized large language model developed by Microsoft, designed to assist developers in code generation, summarization, translation, and repair across multiple programming languages. As part of the WaveCoder series, it aims to enhance coding efficiency and accuracy through instruction-following learning.

HumanEval Score: 81.7%

Key Features:

• Parameter Count: 6.7 billion parameters, balancing performance and computational efficiency.
• Instruction Tuning: Trained using a generator-discriminator framework to follow prompts accurately for precise code generation.
• Multi-Task Proficiency: Capable of handling code generation, summarization, translation, and repair tasks.
• High Benchmark Performance: Achieved a HumanEval score of 79.9, indicating strong code generation capabilities.

Conversations around the Model:

Reddit Post, Twitter Post

3) Deepseek-coder-6.7B-Instruct

The DeepSeek-Coder-6.7B-Instruct is a specialized large language model developed by DeepSeek AI, designed to assist developers in code generation, completion, and infilling across multiple programming languages. As part of the DeepSeek-Coder series, it is tailored to enhance coding efficiency and accuracy.

HumanEval Score: 78.6%

Key Features:

• Parameter Count: 6.7 billion parameters, balancing performance and computational efficiency.
• Instruction Tuning: Fine-tuned on 2 billion tokens of instruction data to follow prompts accurately for precise code generation.
• Long-Context Support: Equipped with a 16,000-token window size, enabling effective handling of large codebases and project-level code completion.
• Multi-Language Proficiency: Trained on a dataset comprising 87% code and 13% natural language in both English and Chinese, making it proficient in over 80 programming languages.
• Extensive Training Data: Pre-trained on 2 trillion tokens, enhancing its code reasoning and generation capabilities.

Conversations around the Model:

Redddit Post 1, Reddit Post 2

4. Phi-3.5-mini-instruct

The Phi-3.5-mini-instruct is a lightweight, multilingual large language model developed by Microsoft, designed to assist developers in code generation, debugging, and understanding across various programming languages. As part of the Phi-3.5 series, it emphasizes efficiency and broad language support.

HumanEval Score: 62.8% 

Key Features:

• Parameter Count: 3.8 billion parameters, offering a balance between performance and resource efficiency.
• Extended Context Length: Supports up to 128,000 tokens, enabling effective handling of extensive codebases and complex programming tasks.
• Multilingual Proficiency: Designed to support multiple languages while maintaining strong English performance across various tasks.
• High-Quality Training Data: Built upon datasets comprising synthetic data and filtered publicly available websites, focusing on high-quality, reasoning-dense information.

Conversations around the Model:

Reddit Post 1, Reddit Post 2

5. Code Llama 7B

The Code Llama 7B is a specialized large language model developed by Meta, designed to assist developers in code generation, understanding, and completion across various programming languages. As part of the Code Llama series, it builds upon the Llama 2 framework to enhance coding efficiency and accuracy.

HumanEval Score: 55%

Key Features:

• Parameter Count: 7 billion parameters, balancing performance and computational efficiency.
• Training Data: Trained on an extensive dataset of 500 billion tokens, with an additional 100 billion tokens specifically for Python, enhancing its proficiency in code generation and understanding.
• Long-Context Support: Handles sequences up to 16,000 tokens, enabling effective processing of extensive codebases and complex programming tasks.
• Model Variants: Available in base, Python-specialized, and instruction-tuned versions to cater to diverse coding requirements.

Conversations around the Model:

Reddit Post

Final Thoughts: Which Model Should You Choose?

Each of these models brings something unique to the table, and the best choice depends on your specific needs:

With the rapid evolution of open-source coding models, developers now have access to powerful AI assistants without relying on closed-source solutions. The future of AI-powered coding is open, and these models are leading the way. 🚀