Best LLMs for Coding in 2025: Local Models That Actually Help

Best LLMs for Coding in 2025: Local Models That Actually Help

Not all LLMs are created equal for coding tasks. A model that writes beautiful prose may produce subtly broken Python. The best coding models in 2025 have been specifically trained or fine-tuned on code, and the gap between a general model and a code-specialized one is significant. Here's what's worth running locally for software development.

Hardware note: Coding models benefit from larger context windows, which require more VRAM. For the best coding experience, 16GB+ VRAM is recommended. LLMHardware.io's coding hardware guide covers the sweet-spot GPUs.

Top Picks by Size

7B class: DeepSeek-Coder-V2 Lite 16B leads the pack and runs well on 10GB VRAM with Q4 quantization. Codestral 7B from Mistral excels at code completion and supports 32K context. 13-15B class: Starcoder2 15B and DeepSeek-Coder 15B both score impressively on HumanEval. 32B+: DeepSeek-Coder-V2 33B approaches GPT-4 on coding benchmarks and runs well on 24GB VRAM.

Code Completion vs Chat vs Review

Inline completion (fill-in-the-middle) and chat-style coding assistance need different models and settings. FIM-trained models like Codestral and StarCoder2 excel at completing partial code snippets. For code review and explanation, instruction-tuned general models (Llama 3.1 70B, Mistral Large) often outperform code-specific fine-tunes on nuanced analysis. Best practice: use a code-specific model for generation, a general instruction model for explanation and review.

VS Code Integration

Continue (continue.dev) is the leading open-source VS Code extension for local AI coding. It connects to Ollama or any OpenAI-compatible endpoint and provides tab-completion and a chat sidebar. Configure it with DeepSeek-Coder or Codestral for inline completion and Llama 3.1 8B for the chat panel. The combination gives you a completely local GitHub Copilot alternative with no subscription and no data leaving your machine.

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