Long-Horizon-Terminal-Bench: Testing Agent Limits on Long-Horizon Terminal Tasks
Tencent Hunyuan
Introduces a 46-task benchmark for evaluating AI agents on terminal tasks requiring hundreds of steps and minutes to hours of execution. Unlike prior benchmarks with sparse binary rewards, it uses dense intermediate grading to track partial progress. Testing 15 frontier models reveals the strongest achieves only 15.2% pass rate, exposing a large gap in sustained multi-step agent capability.
Why it matters
Current agent benchmarks underestimate real-world difficulty by focusing on short tasks with binary success signals. Even the best available models fail 85% of the time on sustained terminal tasks, providing a concrete direction for agent capability research.
Importance: 3/5
New agent benchmark from Tencent Hunyuan exposing 85% failure rate for best frontier models on sustained terminal tasks — dense grading surfaces where current agents actually break down