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Benchmark Testing Models in AI
Benchmark testing models have become crucial for enterprises, enabling them to select performance types that align with their specific needs. However, not all benchmarks are created equal; many testing models rely on static datasets or controlled testing environments. Researchers from Inclusion AI, affiliated with Alibaba’s Ant Group, have introduced a new model leaderboard and benchmark that emphasizes a model’s performance in real-world scenarios. They contend that large language models (LLMs) require a leaderboard that reflects user interactions and preferences rather than just static knowledge capabilities.
In their research paper, the team outlined the basis for Inclusion Arena, a platform that ranks models according to user preferences. “To address these gaps, we propose Inclusion Arena, a live leaderboard that connects real-world AI applications with state-of-the-art LLMs and multi-turn language models (MLLMs). Unlike crowdsourced platforms, our system randomly initiates model battles during interactive human-AI dialogues in actual applications,” the paper states.
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Unique Aspects of Inclusion Arena
Inclusion Arena distinguishes itself from other model leaderboards like MMLU and OpenLLM by focusing on real-life applications and employing a unique ranking methodology. It utilizes the Bradley-Terry modeling method, similar to the approach taken by Chatbot Arena. Inclusion Arena integrates benchmarks into AI applications to collect datasets and perform human evaluations. While the researchers acknowledge that the initial number of AI-powered applications is limited, they aspire to build an open alliance to broaden the ecosystem.
Most users are familiar with leaderboards that showcase the performance of new LLMs from companies like OpenAI, Google, or Anthropic. VentureBeat has also reported on these leaderboards, with models like xAI’s Grok 3 demonstrating their capabilities by topping the Chatbot Arena leaderboard. The researchers from Inclusion AI assert that their new leaderboard “ensures evaluations reflect practical usage scenarios,” thereby providing enterprises with better insights into the models they are considering.
Methodologies in Ranking
Inclusion Arena draws inspiration from Chatbot Arena by employing the Bradley-Terry method, while Chatbot Arena additionally utilizes the Elo ranking method. Most leaderboards depend on the Elo method to establish rankings and performance. Elo, derived from chess, assesses the relative skill levels of players. Both Elo and Bradley-Terry are probabilistic frameworks, but the researchers argue that Bradley-Terry yields more stable ratings.
“The Bradley-Terry model provides a robust framework for inferring latent abilities from pairwise comparison outcomes,” the paper explains. “However, in practical situations, especially with a growing number of models, exhaustive pairwise comparisons can become computationally prohibitive and resource-intensive. This underscores the need for intelligent battle strategies that maximize information gain within a limited budget.”
To enhance ranking efficiency amidst a plethora of LLMs, Inclusion Arena incorporates two additional components: the placement match mechanism and proximity sampling. The placement match mechanism establishes an initial ranking for newly registered models, while proximity sampling restricts comparisons to models within the same trust region.
How Inclusion Arena Operates
Inclusion Arena’s framework integrates seamlessly into AI-powered applications. Currently, two apps are available on Inclusion Arena: the character chat app Joyland and the educational communication app T-Box. When users engage with these apps, prompts are sent to multiple LLMs for responses. Users then select their preferred answer without knowing which model generated it.
The framework leverages user preferences to create pairs of models for comparison. The Bradley-Terry algorithm is subsequently employed to calculate a score for each model, leading to the final leaderboard. Inclusion AI has limited its experiments to data collected up to July 2025, comprising 501,003 pairwise comparisons.
According to initial experiments with Inclusion Arena, the most effective model is Anthropic’s Claude 3.7 Sonnet, followed by DeepSeek v3-0324, Claude 3.5 Sonnet, DeepSeek v3, and Qwen Max-0125. This data was gathered from two apps with over 46,611 active users, as noted in the paper.