- Published on 6 Jan 2026
- Last updated on 6 Jan 2026
- Reading Time: 4 minutes
Fueling the World’s Most Trusted AI Evaluation Platform
We’re excited to share a major milestone in LMArena’s journey. We’ve raised $150M of Series A funding led by Felicis and UC Investments (University of California), with participation from Andreessen Horowitz, The House Fund, LDVP, Kleiner Perkins, Lightspeed Venture Partners and Laude Ventures.
Today, we’re excited to share a major milestone in LMArena’s journey. We’ve raised $150M of Series A funding led by Felicis and UC Investments (University of California), with participation from Andreessen Horowitz, The House Fund, LDVP, Kleiner Perkins, Lightspeed Venture Partners, and Laude Ventures. This milestone is about something much bigger than raising funding. This year we saw our community grow by over 25x alongside rapid adoption by AI labs who trust this platform as a gold-standard for evaluating real-world model performance.
Momentum Driven by the Community
Since announcing our $100M Seed round last year in May, LMArena has grown far faster than we imagined. In a matter of months, the community has contributed:
Since announcing our $100M Seed round last year in May, LMArena has grown far faster than we imagined. In a matter of months, the community has contributed:
- 50 million votes across text, vision, web dev, search, video and image modalities
- 400+ new model evaluations, spanning both open and proprietary models (so many codenames!)
- 145k open-source battle data points across text, multimodal, expert and occupational categories, and more!
These numbers represent real people shaping how AI is measured. The LMArena community has proven that real-world usage can be the backbone for scalable infrastructure to ensure the responsible deployment of AI.
Why We Raised Now
The increased competition among AI labs has created a critical need for rigorous, reproducible evaluations. AI labs need actionable feedback on how to improve their models, and enterprises need to know which models perform best for them. Our The increased competition among AI labs has created a critical need for rigorous, reproducible evaluations. AI labs need actionable feedback on how to improve their models, and enterprises need to know which models perform best for them. Our first evaluation product launched in September, which has resulted in a huge demand for:
- Real-world performance insights
- Diverse, fresh, and expert-level data
- Rigorous science to understand human judgement
