The Best Local LLM Models for Coding in 2026

Not all local LLMs are created equal. After months of vibecoding with dozens of models, here's our definitive ranking for coding tasks — from small and fast to large and capable.

How We Tested

We ran each model through the same battery of real-world tasks:

• Generate a React component from a description
• Fix a bug in a 50-line TypeScript function
• Explain an unfamiliar code pattern
• Write unit tests for existing code
• Convert code between languages
• Generate SQL from plain English

All tests ran on consumer hardware (RTX 3060 12GB, 32GB RAM) using Ollama.

Tier 1: Daily Drivers

These models are fast enough for interactive use and good enough for most tasks.

Gemma 3 4B

ollama pull gemma3:4b

Our top pick for vibecoding. Google's Gemma 3 punches well above its weight class — it handles React, TypeScript, Python, and SQL with surprising accuracy. Runs fast even on modest hardware.

• Speed: ~40 tokens/sec on GPU, ~15 on CPU
• RAM: 4 GB
• Best for: General coding, component generation, quick iterations
• Weakness: Struggles with very complex multi-step logic

Qwen 2.5 Coder 7B

ollama pull qwen2.5-coder:7b

Purpose-built for code. Alibaba's Qwen Coder series consistently outperforms general-purpose models of the same size on coding benchmarks. Excellent at code completion and generation.

• Speed: ~30 tokens/sec on GPU
• RAM: 6 GB
• Best for: Code generation, completion, refactoring
• Weakness: English explanations can be awkward

Llama 3.1 8B

ollama pull llama3.1:8b-instruct-q4_K_M

The reliable workhorse. Meta's Llama 3.1 handles both code and natural language well, making it ideal for tools that mix generation with explanation.

• Speed: ~30 tokens/sec on GPU
• RAM: 6 GB
• Best for: Chat-based coding, explanations, debugging
• Weakness: Code quality slightly below dedicated coding models

Tier 2: Power Users

These need more hardware but deliver noticeably better results.

DeepSeek Coder V2 16B

ollama pull deepseek-coder-v2:16b

A significant step up in code quality. DeepSeek's models have become the go-to for developers who need local models that approach cloud quality.

• Speed: ~15 tokens/sec on GPU
• RAM: 12 GB
• Best for: Complex code generation, architecture decisions, code review
• Weakness: Slow on CPU-only setups

CodeLlama 13B

ollama pull codellama:13b

Meta's dedicated coding model. Fine-tuned specifically for code tasks, it excels at code completion, infilling, and following technical instructions.

• Speed: ~18 tokens/sec on GPU
• RAM: 10 GB
• Best for: Code completion, infilling, language conversion
• Weakness: Weaker at explaining code in plain English

Tier 3: Maximum Quality

For when you need the best possible results and have the hardware.

Llama 3.1 70B (Q4 Quantized)

ollama pull llama3.1:70b-instruct-q4_K_M

The closest you'll get to cloud model quality locally. This requires serious hardware (40GB+ VRAM or massive RAM for CPU inference) but the results speak for themselves.

• Speed: ~5 tokens/sec on high-end GPU
• RAM: 40 GB
• Best for: Everything — architecture, debugging, review, generation
• Weakness: Slow, requires expensive hardware

Quick Comparison

| Model | Size | RAM | Code Quality | Speed | Overall | |-------|------|-----|-------------|-------|---------| | Gemma 3 4B | 4B | 4 GB | Good | Fast | Best value | | Qwen 2.5 Coder 7B | 7B | 6 GB | Very Good | Good | Best for code | | Llama 3.1 8B | 8B | 6 GB | Good | Good | Most versatile | | DeepSeek Coder V2 16B | 16B | 12 GB | Excellent | Moderate | Best mid-range | | CodeLlama 13B | 13B | 10 GB | Very Good | Moderate | Best completion | | Llama 3.1 70B | 70B | 40 GB | Outstanding | Slow | Best quality |

Our Recommendation

If you have 8GB RAM or less: Gemma 3 4B. No contest.

If you have 16GB RAM with a GPU: Qwen 2.5 Coder 7B for pure code work, Llama 3.1 8B if you also need natural language tasks.

If you have 24GB+ VRAM: DeepSeek Coder V2 16B for daily work, with a smaller model for quick tasks.

How to Switch Models in Ollama

You can have multiple models installed and switch between them:

# List installed models
ollama list

# Run a specific model
ollama run gemma3:4b

# Use via API
curl http://localhost:11434/api/generate -d '{
  "model": "qwen2.5-coder:7b",
  "prompt": "Write a React hook for dark mode toggle",
  "stream": false
}'

The Bottom Line

The best model is the one you'll actually use. Start with Gemma 3 4B — it's fast, small, and surprisingly capable. Upgrade when you hit its limits, not before. The models are free to download, so experiment and find what works for your specific workflow.

All the AI tools on RnR Vibe run on local models through Ollama. Try them out to see what local AI can do.