Running an AI coding assistant locally means no subscription fees, no data leaving your machine, and no rate limits. The open-source models available in 2026 are genuinely competitive with cloud offerings. Here’s what works best.
Update (April 24, 2026): DeepSeek V4 Flash (13B active, MIT) is a strong new option for local coding. See V4 Flash guide.
The ranking
| Rank | Model | Params (active) | RAM (Q4) | Coding quality | Speed |
|---|---|---|---|---|---|
| 🥇 | Qwen 2.5 Coder 32B | 32B | 18 GB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| 🥈 | DeepSeek Coder V3 | ~37B active | 20 GB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
| 🥉 | Gemma 4 26B | 3.8B active | 8 GB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| 4 | Codestral 25.01 | 22B | 14 GB | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| 5 | MiMo V2 Flash | ~15B active | 10 GB | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| 6 | Llama 4 Scout | 17B active | 12 GB | ⭐⭐⭐ | ⭐⭐⭐ |
| 7 | Qwen 2.5 Coder 7B | 7B | 4 GB | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
#1: Qwen 2.5 Coder 32B
The best local coding model, period. Qwen 2.5 Coder was trained specifically for code and it shows. It handles complex refactoring, multi-file changes, and obscure language features better than any other open model.
ollama run qwen2.5-coder:32bStrengths: Multi-language support (Python, TypeScript, Rust, Go, Java), understands project context, excellent at explaining code.
Weakness: Needs 18 GB RAM at Q4. Not for lightweight laptops.
Best for: Professional developers who want a local Copilot replacement.
#2: DeepSeek Coder V3
DeepSeek V3 isn’t a dedicated coding model, but its coding performance rivals specialized models. The MoE architecture keeps it efficient.
ollama run deepseek-v3Strengths: Strong at debugging, test generation, and understanding complex codebases. Good at following instructions.
Weakness: Larger download, slower first-token latency than dedicated coding models.
Best for: Developers who want one model for both coding and general tasks.
#3: Gemma 4 26B
The surprise entry. Gemma 4 isn’t marketed as a coding model, but its 26B MoE variant scores 78.5 on HumanEval — competitive with dedicated coding models while using only 3.8B active parameters.
ollama run gemma4:26bStrengths: Incredibly efficient — runs on 8 GB RAM. Good at code review and explaining code. Multimodal (can read screenshots of code).
Weakness: Not as strong on complex multi-file refactoring as Qwen 2.5 Coder.
Best for: Developers on laptops who want coding help without dedicated hardware. See our setup guide.
#4: Codestral 25.01
Mistral’s dedicated coding model. Codestral is fast and handles fill-in-the-middle (FIM) completion natively — important for IDE integration.
ollama run codestral:22bStrengths: Native FIM support, fast inference, good at code completion (not just generation).
Weakness: Weaker at complex reasoning and multi-step tasks compared to Qwen and DeepSeek.
Best for: Real-time code completion in your IDE.
#5: MiMo V2 Flash
MiMo V2 Flash is Xiaomi’s open-source MoE model. It’s fast and surprisingly capable at coding tasks, especially for its active parameter count.
ollama run mimo-v2-flashStrengths: Fast inference, good at Python and JavaScript, open source. See our local setup guide.
Weakness: Weaker on less common languages (Rust, Haskell). Smaller community than Qwen or Llama.
Best for: Quick coding tasks where speed matters more than depth.
#6: Llama 4 Scout
Meta’s Llama 4 Scout is a general-purpose model that handles coding adequately but doesn’t specialize in it.
Strengths: 10M token context window — can ingest entire repositories. Good at understanding large codebases.
Weakness: Coding benchmarks trail behind dedicated models. Needs more RAM.
Best for: Codebase-wide analysis and understanding, not line-by-line coding.
#7: Qwen 2.5 Coder 7B
The budget option. At 4 GB RAM (Q4), it runs on virtually anything — including a Raspberry Pi.
ollama run qwen2.5-coder:7bStrengths: Tiny, fast, good enough for simple completions and boilerplate.
Weakness: Struggles with complex logic, multi-file context, and less common languages.
Best for: Lightweight code completion on constrained hardware. See best AI models under 4GB RAM.
How to set up local coding AI
Option 1: Ollama + Continue.dev (VS Code)
The easiest setup. Install Ollama, pull a model, install the Continue extension in VS Code:
# Install model
ollama run qwen2.5-coder:32b
# Install Continue.dev extension in VS Code
# Then configure it to use Ollama at localhost:11434Continue.dev gives you tab completion, inline chat, and code actions — similar to GitHub Copilot but running entirely on your machine.
Option 2: Ollama + Cody
Sourcegraph’s Cody extension also supports local models via Ollama. It adds codebase-aware context to your queries.
Option 3: Direct API
Any tool that supports the OpenAI API format works with Ollama’s built-in server:
curl http://localhost:11434/v1/chat/completions \
-d '{"model": "qwen2.5-coder:32b", "messages": [{"role": "user", "content": "Write a Python decorator that retries failed functions 3 times"}]}'Local vs cloud: is it worth it?
| Local (Qwen 2.5 Coder 32B) | GitHub Copilot | Cursor Pro | |
|---|---|---|---|
| Cost | $0/month | $10/month | $20/month |
| Privacy | 100% local | Cloud | Cloud |
| Speed | Depends on hardware | Fast | Fast |
| Quality | 90% of cloud | Baseline | Best |
| Rate limits | None | Yes | Yes |
For a deeper comparison, see our free vs paid AI coding tools analysis and how to replace GitHub Copilot for free.
Hardware recommendations
8 GB RAM laptop: Gemma 4 26B (Q4) or Qwen 2.5 Coder 7B — usable for daily coding.
16 GB RAM laptop/desktop: Qwen 2.5 Coder 32B (Q4) or Codestral — professional-grade local coding.
32+ GB with GPU: DeepSeek V3 or Qwen 2.5 Coder 32B at higher quantization — near-cloud quality.
Check our best GPU for local AI guide if you’re building a dedicated setup, or our cheapest way to run AI locally for budget options.
The verdict
Qwen 2.5 Coder 32B is the best local coding model if you have the hardware. Gemma 4 26B is the best if you don’t — it delivers 80% of the quality at 40% of the hardware cost. Either way, local AI coding in 2026 is good enough to replace a cloud subscription for most developers. For models that need more than 24GB VRAM, cloud GPU providers let you run the largest models on demand.
FAQ
What’s the best AI model for coding locally in 2026?
Qwen 2.5 Coder 32B is the best local coding model if you have 18GB+ VRAM. It handles complex multi-file tasks, understands type systems, and generates production-quality code. For smaller setups, Codestral 22B (12GB VRAM) excels at autocomplete.
Can local AI models replace GitHub Copilot?
Yes, for most developers. Continue.dev + Ollama + Codestral 22B gives you equivalent autocomplete quality. For chat-based assistance, Qwen 2.5 Coder 32B matches Copilot’s capabilities. The main trade-off is initial setup time vs Copilot’s zero-config experience.
How much VRAM do I need for local AI coding?
8GB VRAM runs Qwen 2.5 Coder 14B well enough for daily use. 12GB runs Codestral 22B for excellent autocomplete. 16-18GB runs the best 27-32B coding models. You can also use CPU-only inference with 16GB+ system RAM, but it’s 5-10x slower.
Related: AI Coding Tools Pricing