--- title: The Open Agent Leaderboard | daily.dev description: IBM Research launches the Open Agent Leaderboard, an open benchmark that evaluates full AI agent systems rather than just the underlying models. It combines... canonical: https://daily.dev/posts/the-open-agent-leaderboard-vs2qmkwoy twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Open Agent Leaderboard | daily.dev og:description: IBM Research launches the Open Agent Leaderboard, an open benchmark that evaluates full AI agent systems rather than just the underlying models. It combines... og:url: https://daily.dev/posts/the-open-agent-leaderboard-vs2qmkwoy og:image: https://api.daily.dev/og/posts/vs2QMKWOY.png og:image:alt: The Open Agent Leaderboard og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Open Agent Leaderboard **[Hugging Face](https://daily.dev/sources/huggingface)** · 9 min read · 0 upvotes · 0 comments ## Summary IBM Research launches the Open Agent Leaderboard, an open benchmark that evaluates full AI agent systems rather than just the underlying models. It combines six established benchmarks (SWE-Bench Verified, BrowseComp+, AppWorld, tau2-Bench variants) under a unified protocol and reports both quality and cost per task. Key findings: agent architecture already meaningfully impacts results beyond model choice, general-purpose agents are competitive with specialized ones, and failed runs cost 20–54% more than successful ones. The accompanying Exgentic framework lets anyone reproduce or submit evaluations, and everything is open-sourced from day one. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [ITBench-AA: Frontier Models Score Below 50% on the First Benchmark for Agentic Enterprise IT Tasks — by Artificial Analysis and IBM](https://daily.dev/posts/itbench-aa-frontier-models-score-below-50-on-the-first-benchmark-for-agentic-enterprise-it-tasks--inm3wceim) · Hugging Face · 1 upvotes · 0 comments - [5 Facts About AI Coding Agents from Comprehensive Benchmarking](https://daily.dev/posts/5-facts-about-ai-coding-agents-from-comprehensive-benchmarking-wsmhngyjz) · DevOps.com · 0 upvotes · 0 comments - [The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents](https://daily.dev/posts/the-agentic-analytics-benchmark-measuring-model-accuracy-and-efficiency-in-analytical-agents-81a3conc5) · ClickHouse · 0 upvotes · 0 comments --- Tags: [#ai](https://daily.dev/tags/ai), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents) [View this post on daily.dev](https://daily.dev/posts/the-open-agent-leaderboard-vs2qmkwoy) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Open Agent Leaderboard","url":"https://daily.dev/posts/the-open-agent-leaderboard-vs2qmkwoy","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-open-agent-leaderboard-vs2qmkwoy"},"datePublished":"2026-05-18T14:17:20.959Z","dateModified":"2026-08-24T07:10:58.700Z","description":"IBM Research launches the Open Agent Leaderboard, an open benchmark that evaluates full AI agent systems rather than just the underlying models. 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