Local LLMs can assist with coding: generating boilerplate, reviewing code, writing tests, and explaining functions. As of April 2026, models like Qwen2.5-Coder and Llama Code 13B achieve 70–75% accuracy on programming benchmarks. Speed is slower than cloud (2–5 sec per response), but you keep code private.

Key Takeaways

Best coding models (2026): Qwen2.5-Coder 7B (72% HumanEval), Llama Code 13B (74%), Mistral 7B (61%).
Speed: 2–5 seconds per code suggestion. Fast enough for development, slower than GitHub Copilot (~300ms).
Privacy: Code never leaves your machine. Critical for proprietary codebases.
Use cases: Boilerplate generation, code review, test writing, documentation. Not suitable for complex architectural decisions.
As of April 2026, local coding AI is practical for solo developers and small teams.

Best Models for Coding

Model	HumanEval %	VRAM	Inference Speed	Best For
Qwen2.5-Coder 7B	—	4.7 GB	—	Balanced, fastest 7B
Llama Code 13B	—	8.5 GB	—	Higher quality
Mistral 7B	—	4.5 GB	—	Lightweight, EU
DeepSeek-Coder 6.7B	—	4 GB	—	Tiny, efficient

Code Generation Workflow

Prompt the model with function signature + docstring, let it generate implementation.

python

# Prompt design for code generation
prompt = """
Implement the following function:

def merge_sorted_arrays(arr1: List[int], arr2: List[int]) -> List[int]:
    \"\""
    Merge two sorted arrays into a single sorted array.
    Args:
        arr1: First sorted array
        arr2: Second sorted array
    Returns:
        Merged sorted array
    \"\""
    # Implementation:
"""

# Model outputs implementation
# Expected: Two-pointer merge algorithm

Code Review With Local LLMs

Use local LLMs to review code for bugs, style, performance.

Prompt: "Review this code for bugs, security issues, and performance." + code snippet.
Model identifies: unused variables, potential None errors, inefficient loops.
Limitations: Cannot understand complex domain logic or architectural patterns.

Test Generation

Generate unit tests from function implementations.

python

# Prompt for test generation
prompt = """
Write comprehensive unit tests for this function:

[function code]

Generate tests covering:
- Normal cases
- Edge cases
- Error cases

Use pytest format:
"""

# Model generates test_* functions with assertions

IDE Integration

Integrate via VS Code (Continue.dev extension) or Cursor editor.

Inline completions: Ctrl+Shift+\\ triggers local LLM suggestion.

Context: Editor sends surrounding code for better suggestions.

Common Mistakes

Trusting generated code without review. Generated code can have bugs. Always review.
Using models too small. Qwen2.5-Coder 7B is minimum for practical coding. 3B models produce poor code.
Not providing context. Code quality depends on prompt context. Provide function signature, types, docstrings.
Expecting it to understand architecture. Local models understand individual functions, not system design.

Sources

HumanEval Benchmark — github.com/openai/human-eval
Qwen2.5-Coder — github.com/QwenLM/Qwen2.5-Coder

Local LLMs For Coding Workflows: Code Generation, Review, and Testing