Terminal-Bench (AI Coding Benchmark)

Terminal-Bench is an evaluation framework for measuring the performance of AI coding agents in real-world development environments. Unlike traditional code benchmarks that test isolated snippets, Terminal-Bench evaluates the full development cycle: agents must autonomously execute code in a terminal, debug errors, navigate file systems, and solve complex multi-step engineering problems. The framework realistically measures the capabilities of modern coding agents such as Claude Code, GitHub Copilot Workspace, and similar systems under authentic conditions. On Terminal-Bench 2.1 — the current version — Anthropic's Mythos Preview achieved a score of 92.1% with a 4-hour timeout, significantly surpassing the previous benchmark of 82%. A key insight from Terminal-Bench is its sensitivity to compute time: the more time a model is given to work on a task, the higher the success rate tends to be. This reveals that many modern AI coding agents don't have capability gaps — they have compute time limitations. This distinction matters greatly for how teams design, budget, and scale AI-assisted development workflows.