Best OpenSource Coding AI

AI has become the talk of the town nowadays, right? There are tons of AI tools available for different tasks, and new advancements are coming up daily like vibe coding. But how do you actually do vibe coding? Or how do you try out these models? You could use tools like ChatGPT or Claude, but they come with restrictions, and you often need to pay to access full features.

What if you don’t want your data to become part of their training models? That’s where open source coding models come in. You can try them locally, without the internet, and you don’t have to pay for them.

Let’s see Top 7 Open Source AI Coding Tools and how you can try them out.

What Does Open Source AI Coding Tools Mean?

Open source AI coding tools are AI-powered tools (often based on machine learning or LLMs) that help you write, understand, or debug code – and whose source code and/or models are
freely available for anyone to use, study, modify, and share.

What Do These AI Tools Usually Do?

They can help with:

• Code completion

• Code generation (write code from natural language prompts)

• Explaining code

• Refactoring or optimizing code

• Generating tests or documentation

• Code search and navigation

Problems with Proprietary AI Coding Tools

Proprietary AI coding tools like ChatGPT, Claude, Cursor, or GitHub Copilot come with several limitations:

1. Paid & Limited Access

Most proprietary AI coding tools are not fully free. They offer limited usage, and to unlock full capabilities, you need to purchase a subscription or plan.

2. Data Privacy Concerns

When using these tools, your code may be sent to the cloud, which can pose privacy and security risks, especially for companies working with sensitive data. In some cases, your code might even be used to further train these AI models.

3. Other Limitations

• Limited customization

• Vendor lock-in

• Licensing risks

To avoid these issues, many developers prefer using open source coding AI models.

Why should try Open Source Coding Tools

Open source coding tools like Deepseek-coder-v2, code llama, qwen2.5-coder offer several benefits:

• Transparency: You can access model weights and training data, allowing you to run the models locally or fine-tune them as needed.

• Publicly available source code: For many open source models, the backend code, UI, and even editor plugins (like for VS Code) are open for inspection and modification.

• Open licensing: These tools are often released under open source licenses like MIT or Apache 2.0, giving you freedom to use and adapt them without legal restrictions.

Top 7 Best Open Source AI Coding Models

1. Qwen2.5-Coder

   

Qwen2.5-Coder is the code version of Qwen2.5 . A large language model series by the Qwen team from Alibaba Cloud.
It supports code generation, code reasoning, and code fixing. There are six different model sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B.

How to use it?
If you have Ollama installed, you can run this model with one command:

ollama run qwen2.5-coder:0.5

The response will look like this:

Just play with model:

To know more Qwen2.5-coder: https://github.com/QwenLM/Qwen2.5-Coder

2. CodeLlama

CodeLlama is an AI model built specifically for generating and discussing code. As the name implies, it is developed on top of the LLaMA model (by Meta).
It supports many programming languages such as Python, Java, C++, PHP, TypeScript (JavaScript), C#, Bash, and more.

Available sizes: 7B, 13B, 34B, 70B (B = billion parameters).

Run the 7b model using:

bash
ollama run codellama:7b

To know more: https://ai.meta.com/blog/code-llama-large-language-model-coding/

3. DeepSeek-Coder-V2

A few months back, DeepSeek gained a lot of attention as a strong competitor in the AI space. It provides different models for different use cases — including one specifically for coding tasks.
DeepSeek-Coder-V2 is an open-source Mixture of Experts (MoE) model that delivers performance comparable to GPT-4 Turbo.

Note: MoE is a technique in machine learning that combines multiple expert models into a larger, more powerful one.

Available sizes: 16B and 236B

Run the 16B model using:

ollama run deepseek-coder-v2:16b

To know more about: https://github.com/deepseek-ai/DeepSeek-Coder-V2

4. CodeGemma

   

   CodeGemma is a lightweight AI model from Google that supports various tasks like code generation and reasoning.
   It supports languages like Python, JavaScript, Java, Kotlin, C++, C#, Rust, Go, and more.

   Model variants:

   • 7B instruction-tuned

   • 7B pre-trained

   • 2B pre-trained

   Run the model using:

bash
ollama run codegemma:2b

To know more about: https://ai.google.dev/gemma/docs/codegemma

5. Codestral

   

   Codestral is another open source coding AI model developed by the Mistral team.
   Like others, it supports multiple languages including Python, JavaScript, Java, Kotlin, C++, C#, Rust, and Go.

   Currently, Codestral offers one model size: 22B

   Run the model using:

ollama run codestral:22b

6. Granite-Code

   

   Granite-Code models are decoder-style models specifically designed for coding tasks.
   They are developed by the IBM team as part of the Granite series.

   Available sizes: 3B, 8B, 20B, 34B

   Run the model using:

bash
ollama run granite-code:3b

7. Starcoder2

   

   StarCoder2 is a powerful code generation model that supports over 85 programming languages. It’s designed for a wide range of development tasks including code completion, generation, and understanding.

   The model is available in three sizes: 3b, 7b, 15b allowing you to choose based on your resource needs.

   It also supports a context window of up to 16,384 tokens, making it ideal for handling larger codebases and complex prompts.

   Run the model using:

ollama run starcoder2:3b

Risks That Come with All AI Coding Assistants

Regardless of being open or closed source, there are two major concerns when using AI coding models:

1. Security Issues

Most LLMs have knowledge cutoff dates. In the fast-paced world of open source, vulnerabilities get fixed quickly but AI models may not be aware and could generate vulnerable code.

2. Inaccurate Code and Tests

Sometimes, these models generate code that looks correct but is actually buggy, missing edge cases or best practices. Not only for code—if you ask these coding assistants to generate tests, they often can’t write test cases based on your app logic. Plus, you still need to do a lot of copy-pasting.

How Do We Solve Inaccurate Code Test Issues?

Without using ChatGPT or Claude, how can we efficiently generate test cases for your app without doing copy-paste? That’s where Keploy comes in. You can install the Keploy VSCode extension to create unit tests for your app or you can also get it from the GitHub Marketplace.

Does Keploy Actually Use LLMs?

Yes, Keploy uses LLMs for generating unit tests. Let’s say you need unit test cases for your code instead of writing them manually, you can use Keploy to generate the tests for you. In the backend, Keploy uses multiple LLMs with multiple iterations to create possible unit test cases for your code.

To know more about Keploy: https://keploy.io

Conclusion

Open source AI coding tools are a great alternative to proprietary ones, offering transparency, flexibility, and control. The main advantage of using open source AI models is that you can run them locally. In the previous sections, you saw screenshots of how we ran the Qwen models locally without using the internet so your prompts and data never leave your machine. How cool is that, right?

Also, you don’t need to pay to use these models you can run them anytime and still get great responses. If you’re interested, you can even fine-tune the model. Not only can you use Qwen models, but you can also run any of the open source AI models listed above locally with just one command. That’s the superpower of open source AI models compared to proprietary ones.

If needed, you can also use Keploy to write test cases for the code generated by these AI models.

FAQ’s

1. What’s the difference between a general-purpose AI model and a coding-specific AI model?

General-purpose models (like GPT-4) perform a wide range of tasks writing poems, solving math problems, answering questions, etc.
Coding-specific models are trained exclusively for programming tasks and perform much better for software development.

2. Can I fine-tune these open source AI models on my own codebase?

Yes! You can fine-tune or retrain them using your own data which is not possible with most closed-source tools.

3. Can I run these models locally?

Yes! You can use Ollama to run these models locally

4. Does Keploy change my app logic?

No. Keploy doesn’t touch your app logic. It only generates test cases automatically by analyzing your code.

5. Can Keploy fix bugs in my code?

No. Keploy won’t fix your bugs it just writes test cases that detect bugs and ensure your app’s behavior remains correct.