Open-Source Models Explained
Explore the open-source AI ecosystem — who builds these models, what makes them different, and when they might be the better choice for your work.
Open-Source Landscape Map
Click any model card to expand its details. Use the filters below to narrow the landscape by model size, key strengths, or licence type.
Key Insight
Open-source does not mean lower quality. Many open-source models match or exceed closed-source alternatives for specific tasks, especially when fine-tuned on domain-specific data.
Open vs Closed Trade-Off Cards
Position each slider where you think the advantage lies for that dimension. Once you have placed all 8, reveal the expert assessment to compare.
Left = Open-Source Advantage • Right = Closed-Source Advantage
Your Accuracy Score
How closely your placements matched the expert assessment
When to Choose Open-Source
- ✓ Data privacy is critical and you cannot send data to external APIs
- ✓ You need deep customisation or fine-tuning for a niche domain
- ✓ Long-term cost savings matter more than upfront convenience
- ✓ You have in-house technical capability to deploy and maintain models
When to Choose Closed-Source
- ✓ You need top-tier performance right now without setup overhead
- ✓ Vendor support, SLAs, and enterprise compliance are priorities
- ✓ Your team lacks the technical skills to self-host models
- ✓ Speed of deployment is more important than total cost of ownership