⭐ = relative strength for coding tasks (higher is better)
All models can run 100% offline after download using tools like Ollama, GPT4All, LM Studio, Jan, or LocalAI
| Model / Platform | Coding Strength | DevOps / CLI | Python Backend | Frontend (JS/React) | Typical RAM | Notes |
|---|---|---|---|---|---|---|
| Qwen 2.5 Coder (32B) | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | 32 GB+ | One of the strongest local coding models |
| CodeLlama (34B) | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | 32 GB+ | High-quality code generation, slower/heavier |
| Llama 3.1 (70B) | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | 48 GB+ | Massive context, very heavy hardware requirements |
| Qwen 2.5 Coder (14B) | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | 16–24 GB | Best balance of power vs resources |
| DeepSeek Coder (16B) | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐☆ | 16–24 GB | Focused on backend & Python code |
| CodeLlama (13B) | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐☆ | ~16 GB | Solid mid-tier coding model |
| GPT4All (13B / Snoozy) | ⭐⭐☆ | ⭐⭐ | ⭐⭐ | ⭐⭐ | ~16 GB | General-purpose, not coding-specialized |
| Phi-3 Mini (~4B) | ⭐⭐☆ | ⭐⭐☆ | ⭐⭐ | ⭐⭐ | ~8 GB | Very fast, good for small scripts |
| Llama 3.1 (8B) | ⭐⭐☆ | ⭐⭐☆ | ⭐⭐ | ⭐⭐ | 8–16 GB | General-purpose, limited for complex code |
| GPT4All (8B) | ⭐⭐ | ⭐⭐ | ⭐⭐ | ⭐⭐ | 8–12 GB | Lightweight offline assistant |
| GPT4All (3–7B community models) | ⭐⭐ | ⭐⭐ | ⭐⭐ | ⭐⭐ | 4–8 GB | Best for snippets and simple automation |
- Best overall (if you have RAM): Qwen 2.5 Coder 32B
- Best for 16 GB laptops: Qwen 2.5 Coder 14B
- Best lightweight option: Phi-3 Mini
- GPT4All models: Convenient and offline-friendly, but weaker for serious coding compared to coder-optimized models