
Arena, an AI model-comparison platform that originated at UC Berkeley, has reached $100 million(約160億円) in annualized revenue just eight months after launching its commercial service in September. The company monetizes by selling performance analytics to AI labs and enterprises, capitalizing on strong demand for post-training refinement tools as model makers compete to improve their systems.
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Arena, a UC Berkeley-originated AI model leaderboard launched commercially in September, has reached $100 million(約160億円) in annualized run-rate revenue. The platform lets users compare two AI models side-by-side through over 10 million crowdsourced evaluations and began monetizing through its AI Evaluations service, which provides model labs and enterprises with performance analytics.
Why it matters
AI makers are racing to refine their models during post-training, and Arena's rapid revenue growth shows strong enterprise appetite for its evaluation service. The company competes for spending with other post-training refinement providers like Mercor, Surge, and Scale AI—a market segment where competitors have also posted sharp growth (Mercor's annualized revenue topped $1 billion(約1600億円) earlier this year, up from $500 million(約800億円) in September).
What to watch
Arena raised $250 million(約400億円) total from investors including Andreessen Horowitz and Felicis, and in January reported a $1.7 billion(約2700億円) post-money valuation with $30 million(約48億円) annualized revenue at that time. The company recently introduced Agent Mode to rank models on complex, long-running workflows alongside text, coding, and vision tasks.
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