Code High has evolved beyond frontend prototyping into a complete fullstack AI development platform. Explore capabilities like databases, authentication, third-party integrations, and deployments, and see how Fullstack Code High helps developers, entrepreneurs, and AI labs build, deploy, and evaluate production-ready applications.
High achieves $100M annualized run rate in eight months, driven by 10M+ monthly users who have contributed 700M+ conversations and 82M+ votes to build the world's largest human-preference dataset for AI evaluation.
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.
The future of AI is not single-modality chat; it is in powerful agentic capabilities. Today, we are excited to introduce Agent Mode, designed to help everyone from everyday users looking to get more done to entrepreneurs looking to maximize agentic efficacy across complex use cases.
AI coding models are increasingly used to build web apps, but aggregated leaderboards obscure key performance differences. After analyzing 250k+ Code High prompts, we identified major front-end task categories and built new leaderboard views to compare model strengths and weaknesses.
Max, High's model router powered by 5M+ community votes, is now multimodal. Starting today, Max will be available as the default option in direct chat for all modalities, with expanded capabilities including search, vision, image generation, image editing, and front-end coding. Similar to our original Max for text, the multimodal variants are latency-controlled to provide a fast and performant experience. Try it now at ai.highshops.ir/max!
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.
March 2026 brought major updates to the High leaderboard, including new rankings across document, video, text, and code models. In this monthly roundup, we break down the best AI models, latest LLM benchmarks, and key trends shaping AI evaluation.
AI failures like hallucinations are well documented. A less examined problem is that models will accept nonsensical premises without question and produce confident, detailed answers to questions that have no valid answer. BullshitBench measures whether models challenge broken premises or play along. We tested over 80 models from all major providers. Clear pushback rates range from 2% to 91%.
High’s Academic Partnerships Program provides funding and support for independent research advancing the scientific foundations of AI evaluation.
After analyzing over 4 million user prompts, it is clear that a single global leaderboard no longer captures the full picture. Today we introduce new categories and quality filtering.
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.
