Other Notable Open Source Models for Coding
Better Models for TypeScript/JavaScript
After working with Code Llama for a while, I started testing other models and found several that are significantly better for TypeScript and JavaScript development. Here are the ones I actually use and recommend.
Models I Use Daily
DeepSeek Coder: My Current Favorite
DeepSeek Coder 6.7B Instruct has become my go-to model for TypeScript work. It consistently generates better TypeScript code than Code Llama, understands modern JavaScript patterns, and runs well on my hardware.
Why I like it for TypeScript/JS:
- Actually understands TypeScript syntax and generates proper interfaces
- Good with React/Next.js patterns
- Handles async/await and modern ES6+ features correctly
- Fast responses, doesn’t require monster hardware
| Memory: 4GB VRAM | Speed: Very fast | TypeScript Quality: 9/10 |
Mistral 7B: The Reliable Backup
Mistral 7B isn’t specifically a coding model, but it’s surprisingly good at JavaScript and decent with TypeScript. I use it when I need quick answers or when DeepSeek is busy.
Why it works:
- Fast and efficient
- Good understanding of modern JavaScript
- Decent with simple TypeScript
- Works well for explaining code and debugging
| Memory: 4GB VRAM | Speed: Very fast | TypeScript Quality: 7/10 |
Qwen2.5-Coder: For Complex Projects
Qwen2.5-Coder 7B excels when I’m working on complex TypeScript projects with advanced type manipulations. It’s better than most models at understanding complicated generic types and utility types.
Strengths for TS/JS:
- Excellent with advanced TypeScript features
- Good understanding of modern frameworks
- Solid with JSON Schema and API typing
- Better at architectural decisions
| Memory: 4GB VRAM | Speed: Fast | TypeScript Quality: 8.5/10 |
Models I’ve Tested But Don’t Use Regularly
Phi-3: Good for Lightweight Tasks
Phi-3 Mini (3.8B) is impressive for its size but limited for serious TypeScript work. Fine for basic JavaScript, struggles with complex TypeScript.
StarCoder2: Decent but Not Special
StarCoder2 is okay but doesn’t offer anything special over the models above for TypeScript/JavaScript. It’s more focused on breadth across languages.
Yi-Coder: Bilingual But Limited
Yi-Coder is solid but I don’t need the Chinese language support, and it doesn’t outperform DeepSeek or Qwen for my use cases.
My Real-World Performance Rankings
Based on months of daily use with TypeScript/React projects:
| Model | Size | TypeScript Quality | JavaScript Quality | React/Next.js | Memory | My Rating |
|---|---|---|---|---|---|---|
| DeepSeek Coder 6.7B | 6.7B | Excellent | Excellent | Very Good | 4GB | 9/10 |
| Qwen2.5-Coder 7B | 7B | Excellent | Very Good | Very Good | 4GB | 8.5/10 |
| Mistral 7B | 7B | Good | Very Good | Good | 4GB | 7.5/10 |
| DeepSeek Coder 33B | 33B | Outstanding | Outstanding | Excellent | 20GB | 9.5/10 |
| StarCoder2 15B | 15B | Good | Good | Fair | 8GB | 7/10 |
| Phi-3 Mini 3.8B | 3.8B | Fair | Good | Fair | 2GB | 6/10 |
What I Use Each Model For
Daily TypeScript Development: DeepSeek Coder 6.7B Complex Type Definitions: Qwen2.5-Coder 7B or DeepSeek 33B if I have the resources Quick JavaScript Questions: Mistral 7B Code Reviews and Explanations: Any of the top 3 Learning New Frameworks: DeepSeek Coder models
Hardware-Based Recommendations
If you have 4-8GB VRAM: DeepSeek Coder 6.7B Instruct If you have 8-16GB VRAM: Try the larger models like StarCoder2 15B If you have 20GB+ VRAM: DeepSeek Coder 33B is the best I’ve tested If you’re resource-constrained: Phi-3 Mini, but manage expectations
Why These Beat Code Llama for TypeScript
The main reasons these models work better for my TypeScript/JavaScript work:
- Better training data: More modern JavaScript/TypeScript in training
- Framework awareness: Better understanding of React, Next.js, Node.js patterns
- Type system understanding: Proper grasp of TypeScript’s type system
- Modern syntax: ES6+, async/await, optional chaining, etc.
Code Llama was groundbreaking but these newer models have learned from better, more recent code examples.
Modern Coding Model Landscape
The coding AI landscape has evolved rapidly, with several key developments:
- Specialized Coding Models: Purpose-built for programming tasks
- Efficient Small Models: High performance in smaller packages
- Multi-Language Excellence: Better support for diverse programming languages
- Commercial-Friendly Licensing: More options for enterprise use
Let’s explore the most significant alternatives to Code Llama.
DeepSeek Coder: The Performance Leader
DeepSeek Coder has emerged as one of the strongest coding models available, often outperforming much larger models while being incredibly efficient. Developed by DeepSeek AI, this model comes in multiple sizes (1.3B, 6.7B, and 33B parameters) and offers both base and instruction-tuned variants.
What makes DeepSeek Coder special is its exceptional performance-to-size ratio. The 6.7B instruction model achieves scores comparable to much larger models on coding benchmarks like HumanEval, while requiring only 4GB of VRAM. This makes it an excellent choice for developers with limited hardware resources who don’t want to compromise on code quality.
Key strengths include:
- Exceptional HumanEval scores: 78.6% pass@1 for the 6.7B instruct model
- Multi-language support: Strong performance across Python, JavaScript, Java, C++, and more
- Efficient architecture: Outstanding performance per parameter
- Commercial-friendly licensing: Available for commercial use
Mistral Family: Efficiency and Quality
The Mistral family of models has gained significant traction for their balance of efficiency and capability. Mistral 7B, while not specifically designed for coding, shows remarkably strong programming performance and is extremely efficient to run.
Mistral 7B is particularly noteworthy because:
- Runs well on consumer hardware (4GB VRAM)
- Provides solid coding capabilities alongside general intelligence
- Apache 2.0 license makes it fully commercial-friendly
- Fast inference speeds make it practical for real-time assistance
Mixtral 8x7B takes this further with a Mixture of Experts architecture, effectively providing the capability of a 46B parameter model while only activating 12.9B parameters per token. This results in better performance than Mistral 7B while maintaining reasonable resource requirements.
Microsoft Phi-3: Small Model Revolution
Microsoft’s Phi-3 family represents a breakthrough in small model capability. These models demonstrate that with high-quality training data and advanced techniques, smaller models can achieve impressive results.
Phi-3 Mini (3.8B parameters) is particularly remarkable because:
- Runs efficiently on mobile devices and edge hardware
- Requires only 2GB of VRAM
- Provides surprisingly good coding capabilities for its size
- MIT license allows unrestricted commercial use
- Perfect for resource-constrained environments
The Phi-3 family includes Mini (3.8B), Small (7B), and Medium (14B) variants, allowing you to choose the right balance of capability and efficiency for your specific needs.
Qwen2.5-Coder: Multi-Language Excellence
Qwen2.5-Coder, developed by Alibaba, represents one of the most capable coding models available. It comes in multiple sizes (1.5B, 7B, and 32B) and excels particularly at multi-language programming tasks.
Qwen2.5-Coder’s strengths include:
- Outstanding multi-language support: Excellent performance across dozens of programming languages
- Strong reasoning capabilities: Particularly good at complex algorithmic problems
- Bilingual training: Strong performance in both English and Chinese contexts
- Apache 2.0 license: Fully commercial-friendly
- Multiple model sizes: Options for any hardware configuration
StarCoder2: Community-Driven Excellence
StarCoder2, from the BigCode project, represents the power of community-driven development. This model was trained on “The Stack v2” dataset and offers exceptional multi-language coding capabilities.
Key features:
- Transparent development: Open training process and datasets
- Multi-language focus: Designed specifically for diverse programming languages
- Community support: Backed by Hugging Face and the broader community
- Flexible licensing: BigCode OpenRAIL license allows most commercial uses
- Multiple sizes: 3B, 7B, and 15B parameter variants
StarCoder2 is particularly strong for developers working in polyglot environments or those who need a model with transparent training provenance.
Cohere Command R is another versatile model, designed as a general-purpose tool that excels at a wide variety of tasks, including coding. Unlike models that are specifically tailored to coding, Cohere Command R’s strength lies in its adaptability. It’s particularly effective in environments where coding is just one of many tasks, such as in data analysis, content generation, or even creative writing. This model can handle coding tasks efficiently while also offering broad utility in other domains, making it ideal for developers who need a flexible tool that can do more than just code.
Cohere Command R’s general-purpose design makes it a strong choice for developers who require a model that can switch between different types of tasks without losing performance. Whether you’re working on a multi-functional project that involves coding, documentation, and data manipulation, or you need a model that can generate content and analyze data in addition to coding, Cohere Command R provides a balanced solution. Its ability to adapt to various tasks makes it particularly useful in dynamic environments where the scope of work frequently changes.
Other Notable Models
Yi-Coder (01.AI)
A bilingual coding model with strong Chinese and English capabilities, available in 1.5B and 9B sizes. Excellent for international development teams.
Granite Code (IBM)
IBM’s enterprise-focused coding models (3B, 8B, 20B) designed for business applications with strong emphasis on safety and reliability.
OpenCoder
A fully open model with transparent training, available in 1.5B and 8B sizes. Ideal for research and custom fine-tuning projects.
Updated Comparison Table
Here’s a detailed comparison of modern coding models:
| Model | Size | HumanEval | Memory | Speed | License | Best For |
|---|---|---|---|---|---|---|
| DeepSeek Coder Instruct | 6.7B | 78.6% | 4GB | Very Fast | Custom* | Balanced performance |
| Code Llama Python | 34B | 76.8% | 20GB | Fast | Custom* | Python specialization |
| Qwen2.5-Coder | 7B | 72.4% | 4GB | Fast | Apache 2.0 | Multi-language projects |
| Mistral 7B | 7B | 70.2% | 4GB | Very Fast | Apache 2.0 | General purpose + coding |
| StarCoder2 | 15B | 69.1% | 8GB | Moderate | BigCode OpenRAIL | Community projects |
| Phi-3 Medium | 14B | 68.5% | 8GB | Fast | MIT | Efficient deployment |
| Yi-Coder | 9B | 66.3% | 5GB | Fast | Apache 2.0 | Bilingual development |
| Phi-3 Mini | 3.8B | 61.4% | 2GB | Very Fast | MIT | Resource-constrained |
| DeepSeek Coder | 1.3B | 56.8% | 1GB | Very Fast | Custom* | Ultra-efficient |
*Check specific license terms for commercial use restrictions.
Hardware Recommendations by Model
For 8GB VRAM or Less
- Phi-3 Mini (3.8B): Excellent efficiency, runs anywhere
- DeepSeek Coder 1.3B: Surprisingly capable for its size
- Mistral 7B: Great balance of general intelligence and coding
- Qwen2.5-Coder 7B: Strong coding capabilities
For 16GB VRAM
- DeepSeek Coder 6.7B: Outstanding performance per resource
- StarCoder2 15B: Excellent multi-language support
- Phi-3 Medium 14B: Microsoft’s balanced offering
- Yi-Coder 9B: Great for international teams
For 32GB+ VRAM
- Code Llama 34B: Top-tier coding performance
- DeepSeek Coder 33B: Excellent quality and reasoning
- Qwen2.5-Coder 32B: Full feature set
- Mixtral 8x7B: Mixture of experts architecture
Choosing the Right Model
The best model for you depends on several factors:
- Hardware constraints: Start with what fits your VRAM
- Use case: General coding vs. specific language specialization
- Licensing needs: Commercial use requirements
- Performance requirements: Speed vs. quality trade-offs
For most developers starting with local LLMs, I recommend beginning with DeepSeek Coder 6.7B Instruct or Mistral 7B, as they offer excellent performance while being accessible on common hardware configurations.