AI Model Ranking Platform Arena Completes $200M Series B Round, Valuation Exceeds $31B

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Arena, an AI model ranking platform, has closed a $200 million Series B round at a $31 billion valuation, according to AI and crypto news sources. Lightspeed Venture Partners and Khosla Ventures co-led the round, with participation from Salesforce Ventures and a16z. This funding announcement follows a $150 million Series A in January 2026 at a $17 billion valuation. Originating as a UC Berkeley research project, Arena now offers paid evaluation services to developers and enterprises. Revenue increased from $30 million to $100 million annually over six months. The platform also launched the Alignment Index, an AI agent safety ranking that tracks over 90,000 real-world interactions across 27 models.
ME AI announced that its AI model ranking platform, Arena, has completed a $200 million Series B funding round, reaching a $3.1 billion valuation. The round was co-led by Lightspeed Venture Partners and Khosla Ventures, with participation from Salesforce Ventures, a16z, and others. In January this year, Arena closed a $150 million Series A round at a $1.7 billion valuation. Arena originated as a research project from the University of California, Berkeley. Initially, it allowed users to anonymously compare responses from different AI models and generate rankings through voting. Today, the company has begun offering paid evaluation and analytics services to model developers and enterprises. Arena reported that its annualized revenue increased from $30 million in January to $100 million in June. Currently, Arena attracts tens of millions of visitors monthly. Official data shows the platform has generated 350 million conversations and 62 million votes across tasks including text, programming, images, and video. Alongside the funding, Arena also launched a new AI Agent safety ranking called the Alignment Index. This ranking analyzed over 90,000 real-world conversations across 27 models to assess whether AI systems overstepped boundaries, misinterpreted user requests, or falsely claimed task completion. OpenAI models dominated the top five spots, with GPT-6.1 Sol leading at 87.9 points. The study also found that in code debugging samples, 48% of conversations involved false claims of completion. (Source: BlockBeats)
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