The Models I Actually Use for TypeScript/JavaScript

After testing dozens of coding models over the past year, here’s my real-world comparison based on daily TypeScript and JavaScript development. This isn’t about benchmarks - it’s about which models actually help me write better code faster.

My Current Setup

I use different models for different coding tasks. Here’s what works in practice:

Primary Model: DeepSeek Coder 6.7B Instruct (daily driver) Complex TypeScript: Qwen2.5-Coder 7B or DeepSeek 33B Quick JS Questions: Mistral 7B Learning New Frameworks: DeepSeek models

TypeScript/JavaScript Performance Rankings

Based on months of real development work, not artificial benchmarks:

Tier 1: Daily Drivers

Model Size TypeScript React/Next.js Node.js Memory My Rating
DeepSeek Coder 6.7B 6.7B Excellent Very Good Good 4GB 9/10
Qwen2.5-Coder 7B 7B Excellent Very Good Good 4GB 8.5/10
DeepSeek Coder 33B 33B Outstanding Excellent Excellent 20GB 9.5/10

Tier 2: Solid Options

Model Size TypeScript React/Next.js Node.js Memory My Rating
Mistral 7B 7B Good Good Very Good 4GB 7.5/10
Qwen2.5-Coder 32B 32B Outstanding Excellent Excellent 20GB 9/10
StarCoder2 15B 15B Good Fair Good 8GB 7/10

Tier 3: Limited Use Cases

Model Size TypeScript React/Next.js Node.js Memory My Rating
Phi-3 Mini 3.8B 3.8B Fair Fair Good 2GB 6/10
Yi-Coder 9B 9B Good Fair Good 5GB 6.5/10
Code Llama 34B 34B Fair Poor Fair 20GB 6/10

What Each Model Is Actually Good At

DeepSeek Coder 6.7B - My Daily Driver

What it excels at:

  • Writing clean TypeScript interfaces and types
  • React component patterns and hooks
  • Modern JavaScript (ES6+, async/await)
  • API integration and JSON handling

Real examples where it shines:

  • Generating TypeScript types from JSON schemas
  • Creating proper React component structures
  • Writing Node.js API endpoints with correct typing

Qwen2.5-Coder - For Complex TypeScript

What it excels at:

  • Advanced TypeScript features (conditional types, mapped types)
  • Complex generic type manipulations
  • Large-scale TypeScript architecture decisions

When I use it:

  • Building complex type systems
  • Refactoring large TypeScript codebases
  • Working with advanced TypeScript patterns

Mistral 7B - The Quick Helper

What it excels at:

  • Fast responses for simple questions
  • Basic JavaScript patterns
  • Explaining code and debugging

When I use it:

  • Quick syntax questions
  • Simple script generation
  • Code explanations and documentation

Hardware-Based Recommendations

8GB VRAM or Less

Primary Choice: DeepSeek Coder 6.7B

  • Runs smoothly on 4GB VRAM
  • Best TypeScript quality in this range
  • Fast enough for real-time coding assistance

Backup Option: Mistral 7B

  • Also 4GB VRAM
  • Good for quick questions
  • Faster responses but lower TypeScript quality

16GB VRAM

Upgrade to: StarCoder2 15B or keep DeepSeek 6.7B

  • StarCoder2 is good but not significantly better than DeepSeek
  • I usually stick with DeepSeek 6.7B for speed

20GB+ VRAM

Go for: DeepSeek Coder 33B or Qwen2.5-Coder 32B

  • Significant quality improvement over smaller models
  • Worth the extra resources for complex projects
  • DeepSeek 33B is my preference

Real-World Use Cases

Building a React App

Best Model: DeepSeek Coder 6.7B

  • Understands React patterns
  • Good with TypeScript props and state
  • Handles modern React features (hooks, context, suspense)

TypeScript Library Development

Best Model: Qwen2.5-Coder 7B or DeepSeek 33B

  • Better with complex type definitions
  • Good architectural decisions
  • Understands publishing and packaging

Node.js API Development

Best Model: DeepSeek Coder (any size)

  • Solid Express/Fastify patterns
  • Good with TypeScript decorators
  • Understands async patterns well

Learning New Framework

Best Model: DeepSeek Coder models

  • Good at explaining concepts
  • Provides working examples
  • Understands modern JavaScript ecosystem

Speed vs Quality Trade-offs

For Real-time Assistance: Mistral 7B or DeepSeek 6.7B For High-Quality Code: DeepSeek 33B or Qwen2.5-Coder 32B Best Balance: DeepSeek Coder 6.7B

Models I Don’t Recommend for TypeScript/JS

Code Llama: Dated JavaScript patterns, poor TypeScript understanding Phi-3 Mini: Too limited for serious TypeScript work StarCoder (original): Superseded by newer models General models without coding focus: Inconsistent code quality

My Setup Evolution

Started with: Code Llama 7B (disappointing for TypeScript) Moved to: DeepSeek Coder 6.7B (game changer) Added: Qwen2.5-Coder 7B for complex tasks Current: DeepSeek 6.7B primary, DeepSeek 33B for complex projects

The key insight: model size matters less than training quality for TypeScript/JavaScript. A well-trained 6.7B model beats a poorly-trained 34B model every time.

Best choices:

  1. Code Llama Python 34B: If you have the resources
  2. DeepSeek Coder Instruct 6.7B: Excellent Python performance
  3. Qwen2.5-Coder 7B: Strong Python with multi-language support

For Multi-Language Projects

Recommended:

  1. StarCoder2 15B: Built for multi-language scenarios
  2. Qwen2.5-Coder: Excellent multi-language support
  3. Code Llama Base: Solid general programming support

For Enterprise Use

Production-ready options:

  1. Granite Code: IBM’s enterprise-focused model
  2. DeepSeek Coder 33B: High quality, stable performance
  3. Mistral Small 22B: Commercial-friendly licensing

For Research and Experimentation

Open and flexible:

  1. OpenCoder: Transparent training and open weights
  2. StarCoder2: BigCode project, community-driven
  3. Code Llama: Meta’s open model family

Licensing Considerations

Commercial Use Friendly

Model License Commercial Use Notes
Mistral Models Apache 2.0 Yes Fully commercial friendly
Phi-3 Family MIT Yes Microsoft’s open license
Qwen2.5 Apache 2.0 Yes Alibaba’s open model
StarCoder2 BigCode OpenRAIL Yes* Some restrictions on harmful use
DeepSeek Coder Custom License Yes* Check specific terms

Restricted Commercial Use

Model License Commercial Use Notes
Code Llama Custom License Limited Restrictions for large companies
Yi-Coder Apache 2.0 Yes Generally permissive

Important Notes:

  • Always check the latest license terms before deployment
  • Large companies may have additional restrictions
  • Some models require attribution
  • Consider liability and support requirements for production use

Performance Optimization Tips

Memory Optimization

  1. Use Quantization:

    • GGUF format for CPU inference
    • AWQ/GPTQ for GPU inference
    • Reduces memory by 50-75%

  2. Model Serving:

    • Use vLLM for efficient GPU serving
    • Implement model caching for repeated queries
    • Consider model parallelism for large models

Speed Optimization

  1. Hardware Selection:

    • Prefer models that fit entirely in VRAM
    • Use fast SSDs for model loading
    • Consider Apple Silicon for efficient inference

  2. Inference Optimization:

    • Batch requests when possible
    • Use streaming for better UX
    • Implement prompt caching

Emerging Patterns

  1. Smaller, More Efficient Models: Phi-3 series leading the way
  2. Mixture of Experts: Mixtral showing promise for specialized tasks
  3. Multi-Modal Capabilities: Integration of code, text, and visual understanding
  4. Domain Specialization: Models fine-tuned for specific programming languages or frameworks

Expected Developments

  1. Better Reasoning: Enhanced logical and mathematical capabilities
  2. Longer Context: Support for entire codebases and large projects
  3. Tool Integration: Native support for development tools and APIs
  4. Collaborative Features: Models designed for team development workflows

Last updated: July 20, 2025

Note: Benchmark scores and specifications may vary based on evaluation methodology and hardware configuration. Always test models in your specific environment for accurate performance assessment.