Open Source AI
The open model ecosystem — Llama, DeepSeek, Qwen, and the open vs. closed debate
45%
Qwen share of downloads
HuggingFace, Dec 2025
1M+
Models on HuggingFace
Jan 2026
$6M
DeepSeek R1 training cost
Reported
405B
Llama 3.1 parameters
Largest open model
Open source AI is catching up. The gap between open and closed models has shrunk from 2 years to 3-6 months. DeepSeek shocked the industry with efficient training. Meta's Llama and Alibaba's Qwen lead downloads. The debate over open vs. closed continues.
The Open Source Landscape
Open source AI spans a spectrum from "open weights" (downloadable model files) to truly open source (weights + training code + data). Most frontier open models are open-weight only.
Open ≠ Open Source: Models like Llama are "open weight" — you can download and run them, but training data and full methodology remain proprietary. True open source (like OLMo) includes everything needed to reproduce training.
Leading Open Models
| Model | Creator | Parameters | Strengths |
|---|---|---|---|
| DeepSeek R1 | DeepSeek | 671B (MoE) | Reasoning, efficiency |
| Llama 3.1 | Meta | 8B/70B/405B | General purpose, ecosystem |
| Qwen 2.5 | Alibaba | Up to 72B | Multilingual, most downloaded |
| Mistral Large 2 | Mistral | 123B | Efficient, MoE architecture |
| Gemma 2 | 9B/27B | Compact, on-device | |
| Phi-3 | Microsoft | 3.8B/14B | Small, efficient |
Key Players
Meta (Llama)
Pioneer of large open models. Strategy: commoditize the model layer to prevent OpenAI/Google from charging monopoly rents. Llama 3 powers thousands of applications.
DeepSeek
Chinese lab that trained frontier models for $6M (vs $100M+ for competitors). Proved algorithmic innovation can beat scale. Caused NVIDIA to briefly lose $600B in market cap.
Alibaba (Qwen)
Now the most downloaded open model family, with 45% of HuggingFace downloads. Strong multilingual support. Available across size range.
Mistral
European champion. Pioneered MoE architecture for open models. $6B valuation. Balancing open source credibility with commercial viability.
The DeepSeek Effect
In January 2025, DeepSeek released R1, a reasoning model trained for approximately $6M that matched or exceeded GPT-4 on many benchmarks. The implications shook the industry.
What It Means: DeepSeek proved that throwing billions at compute isn't the only path to frontier capabilities. Algorithmic efficiency, better data curation, and Mixture of Experts architecture can dramatically reduce costs. This challenges the entire CapEx thesis behind Big Tech's infrastructure buildout.
The Open vs. Closed Debate
Case for Open
• Democratizes access to AI capabilities
• Enables safety research and red-teaming
• Prevents concentration of power
• Drives innovation through competition
• Allows customization and privacy
• Enables safety research and red-teaming
• Prevents concentration of power
• Drives innovation through competition
• Allows customization and privacy
Case for Closed
• Easier to control misuse
• Alignment techniques stay proprietary
• Clearer liability chains
• Protects R&D investment
• National security considerations
• Alignment techniques stay proprietary
• Clearer liability chains
• Protects R&D investment
• National security considerations
✓ Key Takeaways
✓ Open models now 3-6 months behind closed (was 2 years)
✓ Qwen leads downloads; Llama leads ecosystem
✓ DeepSeek proved efficiency beats pure scale
✓ "Open weight" ≠ true open source
✓ Meta's strategy: commoditize the model layer
✓ Regulatory treatment of open models uncertain