Best Ollama Models for Coding 2026

Local coding models used to be a joke. In 2026 a handful are genuinely useful for real dev work. Here are the ones worth pulling.

📜 Table of Contents

• Local Coding Models Grew Up
• The Models
• The Practical Setup
• What They're Actually Good At (2026)
• FAQ

Local Coding Models Grew Up

Two years ago local coding models could autocomplete; today's top ones can scaffold projects, fix bugs with context, and handle multi-file edits. Ollama (178,451 stars, 2026-08-14) serves them all with one command. The honest ranking: quality still trails GPT-4o-class clouds, but for privacy-sensitive or offline work, these are production-viable.

The Models

Qwen 2.5 Coder (7B, 14B, 32B) - the local coding king. Trained specifically on code; the 7B outperforms many larger models on Codeforces-style problems, and 32B is the top local pick. Handles Python, TypeScript, Java, and more. Pull: qwen2.5-coder:7b or :32b.

DeepSeek-Coder / DeepSeek-R1 distilled (7B-32B) - strong at reasoning about code. The R1 distilled versions 'think' before answering, which helps debugging sessions. Slower, but finds bugs the fast models miss.

Llama 3.3 8B / 70B - the generalist that is decent at coding. 8B is fine for autocomplete and simple refactors; 70B approaches useful for complex work if you have the hardware.

gpt-oss:20b - OpenAI's open-weight model: surprisingly capable at code for its size, with a permissive license. The 120B needs serious hardware.

Codestral (Mistral) - Mistral's coding model with a huge 256k context - useful when your codebase context matters.

Phi-4 14B - the compact reasoning model; strong on math-adjacent coding tasks.

Mistral 7B / Mixtral - the classic: solid autocomplete, weaker at complex reasoning.

The Practical Setup

ollama pull qwen2.5-coder:7b
# in Cursor / VS Code / Continue: point the model at http://localhost:11434

Most AI code editors accept an OpenAI-compatible endpoint - Ollama serves one at localhost:11434, so local models drop into Cursor, Continue (35,474 stars), or Cline (66,136 stars) with a config change.

What They're Actually Good At (2026)

• Autocomplete and inline suggestions: all of them, decently.
• Unit test generation: Qwen Coder and DeepSeek-R1 distilled are genuinely good.
• Explaining code: all; R1 distilled explains with reasoning.
• Multi-file refactors: 32B+ models only, and with supervision.
• Full project scaffolding: usable but review everything.

FAQ

Which is the best free coding model for local? Qwen 2.5 Coder 7B - the quality/size ratio is unbeatable.

Do I need a GPU? 7B runs CPU-only (slow) or on 6-8GB VRAM; 32B wants 24GB VRAM.

Can I use them in Cursor? Yes - point Cursor at the Ollama OpenAI-compatible endpoint.

Are local models good enough for production code? For autocomplete and tests, yes; for architecture decisions, have a human review.

❓ FAQ

Which is the best free coding model for local?

Qwen 2.5 Coder 7B - the quality/size ratio is unbeatable.

Do I need a GPU?

7B runs CPU-only (slow) or on 6-8GB VRAM; 32B wants 24GB VRAM.

Can I use them in Cursor?

Yes - point Cursor at the Ollama OpenAI-compatible endpoint.

Are local models good enough for production code?

For autocomplete and tests, yes; for architecture decisions, have a human review.