Research

New Categories for Web Development in Code Arena

New Categories for Web Development in Code Arena

AI coding models are increasingly used to build web apps, but aggregated leaderboards obscure key performance differences. After analyzing 250k+ Code Arena prompts, we identified major front-end task categories and built new leaderboard views to compare model strengths and weaknesses.

Multimodal Max

Multimodal Max

Text Arena Score vs. Time to First Token Note: displayed models were the top public models when the router was created. Search Arena Score vs. Time to First Token Note: displayed models were the top public models when the router was created. Vision Arena Score vs. Time to First Token

Supporting Independent Research in AI Evaluation

Supporting Independent Research in AI Evaluation

Arena’s Academic Partnerships Program provides funding and support for independent research advancing the scientific foundations of AI evaluation.

Introducing Max

Introducing Max

Today we are releasing Max, Arena's model router powered by our community’s 5+ million real-world votes. Max acts as an intelligent orchestrator—it routes each user prompt to the most capable model for that specific prompt.

Studying the Frontier: Arena Expert

Studying the Frontier: Arena Expert

Arena Expert is a great way to differentiate between frontier models. In this analysis, we compare how models perform on 'general' vs 'expert' prompts, focusing on 'thinking' vs 'non-thinking' models.

Arena's Ranking Method

Arena's Ranking Method

Since launching the platform, developing a rigorous and scientifically grounded evaluation methodology has been central to our mission. A key component of this effort is providing proper statistical uncertainty quantification for model scores and rankings. To that end, we have always reported confidence intervals alongside Arena scores and surfaced any

Arena Expert and Occupational Categories

Arena Expert and Occupational Categories

The next frontier of large language model (LLM) evaluation lies in understanding how models perform when challenged by expert-level problems, drawn from real work, across diverse disciplines.