Artificial Analysis launches Coding Agent Index with 3 new benchmarks

The Coding Agent Index by Artificial Analysis provides a standardized way to evaluate coding agents across three rigorous benchmarks. First, SWE-Bench-Pro-Hard-AA consists of 150 challenging, real-world coding tasks sourced from Scale AI's SWE-Bench Pro, designed to test frontier models on difficult bug fixes and feature implementations. The tasks require deep code understanding and precise patch generation. Second, Terminal-Bench v2 includes 84 agentic terminal tasks from the Laude Institute, spanning system administration, cryptography, and machine learning. Five tasks were excluded due to environment incompatibility, leaving a focused set of command-line challenges. Third, SWE-Atlas-QnA features 124 technical questions from Scale AI that demand agents to explore codebases and produce text-based answers about code behavior, root causes, and logic.

This index moves beyond simple code generation to measure true agentic capability—autonomous planning, debugging, and system interaction. By publishing performance comparisons between different model and harness combinations, Artificial Analysis enables developers to identify which setups excel at each task type. For practitioners building AI coding assistants, this benchmark provides actionable insights for model selection and integration strategies. The inclusion of both code writing and terminal tasks reflects the full spectrum of software engineering workflows, from IDE-based development to DevOps automation.