Artificial Intelligence 31 Jan, 2025.
Coding has become faster and easier thanks to advanced AI models known as large language models (LLMs). LLMs for coding can generate code, autocomplete code, fix bugs, and more.
In order to innovate rapidly, it’s important to make programming and development as seamless as possible. Companies now focus on achieving better speed to market without compromising on quality standards or security. Artificial Intelligence (AI) helps ensure that and a lot more.
LLMs are changing how software gets built. They now write and improve code in a semi-automated manner, accelerating development, testing, and deployment.
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However, there are multiple options to choose from, and if that confuses you, this blog is all you need. Here are 8 of the best open-source LLMs for code generation in 2025.
Top 8 Open Source LLMs for Coding in 2025
1. DeepSeek V3
DeepSeek V3 is one of the most capable open-source LLMs built by Anthropic, an AI safety startup. This 671 billion parameter model rivals private LLMs like GPT-4o in both quality and scale.
What makes DeepSeek V3 special is its unprecedented 128,000 token context window. This allows it to deeply understand code context when making suggestions. The model demonstrates excellent accuracy on coding benchmarks.
DeepSeek V3 does have high hardware requirements typical of giant models. It needs to run on specialized H100 GPUs. Operating it can get costly for smaller teams. However, its advanced capabilities make it a top choice for large enterprises.
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2. Llama 3.3 70B Instruct
Llama 3.3 70B Instruct from Meta AI comes next. It has 70 billion parameters tuned specifically for following natural language instructions.
This mid-size open-source LLM balances cost and utility. It outdoes other models of its size on most NLP benchmarks. Plus it has strong mathematical reasoning aptitude useful when working with code.
The model does have a commercial license for all derivative works. This limits what you can do with fine-tuned versions. But it provides ample abilities out-of-the-box for code search, bug detection, optimization, and more.
Read More: How to Build an LLM Like DeepSeek?
3. Phi 3 Mini
Phi 3 Mini is Microsoft’s latest open-source LLM. At just 3.8 billion parameters, it runs lightning-fast on cheap GPUs. This makes it one of the most economical LLMs you can self-host today.
Phi 3 Mini is instruct-tuned like Llama 3. But it actually beats LLMs over 15X its size on certain benchmarks! The model does trade some output quality for smaller sizes and faster speeds. Yet it remains versatile enough for various coding aids.
For teams that want beyond IDE autocomplete, Phi 3 Mini hits the sweet spot between cost, speed, and utility. It can serve coders at scale from readily available hardware.
Read More: DeepSeek vs ChatGPT – A Detailed Comparison
4. Mistral 7B & Mixtral 8X7B
Mistral 7B and Mixtral 8X7B from French startup Mistral AI are two other mid-size open-source coding LLMs gaining traction.
Its 7 billion parameters handles both natural language and code with expertise. It approaches pricier models like Codex on programming tasks while retaining strong English abilities.
Mixtral 8X7B meanwhile uses a mixture-of-experts architecture for 46.7 billion parameters. This allows it to match bigger LLMs in output quality while minimizing computation costs.
Both models have ample context windows to imbibe code structure. Their Apache 2.0 license also permits full commercial use. For capable coding assistance without high overhead, Mistral and Mixtral deliver.
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5. StarCoder
StarCoder from the BigScience project is another emerging coding LLM option. Available LLM sizes range from 3 billion to 15 billion parameters.
Like CodeT5, StarCoder uses a sequence-to-sequence structure for adept coding abilities. Models are trained on vast troves of public code and documentation from open-source projects.
StarCoder offers ample context length to absorb complex code structure. Its training regimen also emphasizes unsupervised learning. This allows it to keep improving without manual tuning.
For an ever-evolving LLM that inherits the collective expertise of thousands of open-source developers, StarCoder is a strong contender. Its self-supervised architecture means it will only get better at coding tasks over time.
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6. Polycoder
Polycoder is a 2.7 billion parameter LLM for code from Anthropic rivaling GPT-4o. This model is specifically optimized for polymorphic code generation.
Polymorphic code generation means writing code that works across multiple frameworks, languages, and environments. Polycoder’s training methodology enhances cross-framework abilities lacking in other coding models.
The model can generate code in over 50 languages interchangeably. It also has robust context-switching capabilities. This allows correctly interpreting prompts for generation in new languages and frameworks.
For polyglot coding assistance with framework flexibility, Polycoder has no match. Its polymorphic specialization solves a major pain point for large enterprises balancing legacy systems alongside modern stacks.
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7. Tabnine
With over 3.5 million developers using it, Tabnine is the world’s most popular coding assistant. Available models range from 117 million to over 2 billion parameters.
Tabnine integrates directly into developer environments like VS Code. Here it provides lightning-fast code completion powered by its proprietary AI models.
The technology combines semantic code search with generative capabilities for smart suggestions. All processing happens locally for privacy and speed.
Smooth environment integration, a huge user base, and client-side operation make Tabnine a hassle-free coding LLM. For teams wanting plug-and-play performance without infrastructure hassles, Tabnine hits the mark.
Read More: Best Open Source Generative AI Models in 2025
8. OpenAI Codex
OpenAI Codex is a formidable AI system for code from OpenAI – the makers of ChatGPT. Details are sparse since OpenAI keeps key product plans private.
Rumors estimate Codex may have between 30 to 60 billion parameters. OpenAI could expand availability through API access or integration with GitHub Copilot.
Codex presumably builds upon OpenAI’s existing injections into software development via Copilot. Its capabilities likely surpass other services with advanced fluency in translating language to and from code.
As a proprietary offering, OpenAI Codex does limit what you can do. But its sheer scale and backing from Microsoft means it is poised to dominate as an AI coding tool. Accessibility hurdles aside, no coding LLM today likely outweighs Codex for capability.
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Build the Next Successful Open Source LLM for Code Generation with Cubix
Advanced LLMs like these will radically evolve coding over the next decade. Engineers can feasibly turn loose specifications into working software with minimal sweat. Non-coders may even gain enough coding assistance from AI to build their apps.
Alongside boosting productivity though, responsible development and testing will remain vital. Software engineering still needs human oversight over code directly impacting individuals and society. But for routine coding tasks, AI promises to magnify developer output manifold.
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If you have high-scale development goals but don’t have the teams needed to accelerate, Cubix can help you out. We utilize AI tools to accelerate our development and delivery cycle while ensuring top-notch quality and functionality across all touchpoints.
Connect with our representatives and we’ll see how we can help you achieve your goals.
Mohammad Azeem
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