Agent Arena measures complex, real-world tasks. Data shows that these long-horizon sessions amplify ...
lmarena.ai(@lmarena_ai) · 人工智能
Agent Arena measures complex, real-world tasks. Data shows that these long-horizon sessions amplify differences in frontier models. Long-horizon tasks in Agent Arena span hundreds of turns, from porting a codebase to Rust and auditing an investment strategy document to writing a math textbook, and more. Comparing the top model from each of the 5 leading labs on Agent Arena reveals a 55-minute gap in how long they spend on long-horizon tasks. The models are: - Claude Opus 5.5 by @AnthropicAI - Gemini 4 Carbon by @GoogleDeepMind - GPT-6 Astra by @OpenAI - Muse Wonder Full by @AIatMeta - Kimi K3.1 Pro by @Kimi_Moonshot In this group, Claude Opus 5.5, #1 in Agent Arena, takes the longest at every task length, peaking at 1 hour 51 minutes. In contrast, Kimi K3.1 Pro spends the least time at every length, with 56 minutes on long-horizon tasks. Your browser does not support the video tag. 🔗 View on Twitter 💬 10 🔄 1 ❤️ 28 👀 10551 📊 13 ⚡ Powered by xgo.ing