Popular AI leaderboard Arena nearly doubles valuation to $3.1B valuation in 10 months

Arena, the company that runs the crowdsourced LMArena AI leaderboard, said on Thursday it raised a $200 million Series B round at a $3.1 billion valuation. The round was led by Lightspeed Venture Partners and Khosla Ventures, with participation from Salesforce Ventures, 01 Advisors, Dell Technologies Capital, Endeavor Catalyst, a16z, Felicis and others.

The company previously announced a $150 million Series A in January at a $1.7 billion post-money valuation, when it said annualized revenue was $30 million. In June, Arena said it reached $100 million in annualized run-rate revenue. The new valuation nearly doubles the January figure in about 10 months.

Arena began in 2023 as a research project at UC Berkeley that crowdsourced rankings of AI models. Its free consumer platform lets people enter prompts or request vibe-coded projects and then rate which model does it better. Arena claims tens of millions of monthly visitors. In September of last year, it launched AI Evaluations, a commercial service that gives model labs and enterprises detailed performance analytics based on community feedback.

Arena also added an alignment category to its leaderboard. It ranks models on unauthorized action (taking actions it wasn't asked to take), false attribution (wrongly crediting statements or facts to the wrong source), and "deceptive completion" (lying about completing tasks it didn't do). In the preliminary alignment leaderboard, a slate of OpenAI's models are at the top, with Claude Opus 5.5 and Claude Fable in sixth and ninth place, respectively.

In its funding announcement, Arena said AI is advancing faster than the ability to evaluate it, and that static benchmarks break down once models recognize they are being tested. It added that the world needs a neutral third party to measure how safe and aligned AI actually is once it is in the hands of real people.

Why it matters: The funding and the new alignment leaderboard give developers and product teams another way to compare models on safety and behavior, not just standard benchmarks, as enterprises seek help choosing models for their own needs.