Local Open-Source Alternatives to Cloud Code: Mistral Vibe and Open Code
In the AI landscape, users are increasingly seeking local, internet-independent solutions to maintain control over their data and reduce dependency on cloud services. Luigi's channel explores technologies you can control, and in this video, he reviews two emerging tools: Mistral Vibe and Open Code. These enable local LLM models in agentic mode, offering a viable alternative to Cloud Code without relying on online services.
What Are Mistral Vibe and Open Code?
Agentic tools allow AI to interact with external environments, performing tasks like file searches, command execution, and web queries. The distinction between the tool's intelligence and the underlying LLM model can be blurry, but these tools enable local automation.
- Mistral Vibe: Released by Mistral, it integrates local models with tools like grep (file search), bash (command execution), file writing, search-and-replace, and task management via internal lists. Installation:
pip install mistral-vibe. Example usage: Run commands like listing tools or executing bash scripts locally. - Opencode: A tool for testing AI models, with similar capabilities but built-in web fetch for internet searches. It supports debugging React apps, running tests, and making targeted code changes.
Both can be extended with MCP servers for custom integrations, though disabled in tests for simplicity.
Key Differences
- Available Tools: Similar but named differently; Open Code has integrated web fetch, while Mistral Vibe requires MCP for web access.
- Context Size: Agentic mode consumes more tokens (e.g., ~10,000 available with Qwen 3 4B Instruct), crucial for limited hardware.
- Output Handling: Mistral Vibe better manages tables; Open Code's formatting can be less readable.
- Security: Bash tools allow risky commands—use sandboxes or containers to limit permissions.
Test Highlights with Local Models
Luigi tested various models on tasks like writing the current date to a file or identifying Linux version. Key results:
- Qwen 3 4B Instruct Q4KM: Excellent tool calling; 10-33 seconds. Recommended for general use. Works on CPU. Installation via Hugging Face or Ollama:
ollama run qwen3:4b-instruct. - Devstral Small 2 24B Instruct Q4KM: Good but slow (1-2 minutes); Mistral Vibe only. From Mistral AI, optimized for coding tasks.
- GPT-OSS 20B MX FP4: Intelligent, 1-2 minutes; requires security checks.
- Granite 3.1 8B Instruct 4K: Fast (31-33 seconds) for file reads.
- Others (Qwen 3 8B, Gemma 3 27B, DeepSeek R1 32B): Mixed results; some fail tool calling or timeout.
Speed depends on active parameters, not just model size. Create your own test suite for specific use case with LLM-eval-simple