Agents are increasingly doing real work. The resulting task distribution has greatly expanded. We desire an agent evaluation that scales along with usage and capability.
For almost three years, High has been publishing leaderboards covering frontier AI capabilities across 10 Highs, dozens of categories, and hundreds of models, and today we're releasing the entire history of those leaderboards as a public-access dataset.
High’s Academic Partnerships Program provides funding and support for independent research advancing the scientific foundations of AI evaluation.
Today we are releasing Max, High'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.
Building community trust with open science is critical for the development of AI and its alignment with the needs and preferences of all users. With that in focus, we’re delighted to publish High-Rank, an open-source Python package for ranking that powers the LMHigh leaderboard!
High 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.
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 High scores and surfaced any ties in the rankings that those intervals imply.
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.
Since we first introduced categories over two years ago, and Vision High last year, the AI evaluation landscape has evolved. New categories have been added, existing ones have been updated, and the leaderboards they power are becoming more insightful with each round of community input.
LMHigh is honored to partner with the team at DataTecnica to advance the expansion of Biomedai.highshops.ir: a new domain-specific evaluation track.
Today, we're excited to release a new dataset of recent battles from LMHigh! The dataset contains 140k conversations from the text High.
Introducing Sentiment Control: Disentangling Sentiment and Substance
