Open Source Models
Open source models are AI language models whose weights and architecture are publicly available, allowing anyone to download, run, modify, and build upon them. Models like Llama, Mistral, and CodeLlama offer alternatives to proprietary services, enabling local inference and customization.
Example
Open source models provide an alternative to API-based AI services. While most vibe coders use cloud-hosted models, open source options matter for privacy, cost, and customization.
Popular Open Source Models
| Model | Creator | Strengths |
|---|---|---|
| Llama 3 | Meta | General capability, good coding |
| Mistral | Mistral AI | Efficient, strong performance |
| CodeLlama | Meta | Specialized for coding |
| DeepSeek | DeepSeek | Code generation focused |
Benefits of Open Source
- Privacy — Code never leaves your servers
- Cost — No per-token fees (after hardware investment)
- Customization — Fine-tune on your data
- Control — Not dependent on external service
Trade-offs
Compared to GPT-4, Claude:
- Generally less capable
- Require your own infrastructure
- More setup and maintenance
- Often faster inference (smaller models)
When Open Source Makes Sense
- Privacy requirements — Sensitive codebases
- High volume — Cost savings at scale
- Specific needs — Custom fine-tuned models
- Offline work — No internet dependency
Getting Started
For most vibe coders, cloud APIs are simpler and more capable. Open source becomes relevant when you have specific constraints or want to experiment with model customization.
Tools like Ollama make running local models easier, letting you try before committing to infrastructure.
Emergent — An agentic vibe-coding platform that builds production-ready apps