Terminal‑Bench 2.0 and its companion framework Harbor have hit the market as a cohesive upgrade aimed at tightening the standards for AI agent evaluation in real‑world terminal environments. The new benchmark replaces the original 1.0 suite with 89 tasks that have undergone hours of manual and LLM‑assisted validation, eliminating flaky dependencies such as the unstable download‑youtube task. By raising the difficulty ceiling and refining task specifications, the developers hope to encourage more reliable, reproducible benchmarks that better reflect the challenges faced by autonomous agents in developer workflows.
Harbor, the runtime framework that accompanies Terminal‑Bench 2.0, is designed to run and evaluate agents in cloud‑deployed containers at scale. It supports any container‑installable agent, offers scalable supervised fine‑tuning and reinforcement learning pipelines, and integrates natively with major providers like Daytona and Modal. The team used Harbor internally to generate tens of thousands of rollouts during benchmark creation, and the public release now includes documentation for submitting agents to a shared leaderboard, making it easier for researchers and developers to compare performance across platforms.
Initial leaderboard results reveal GPT‑5‑based agents leading with a 49.6 % success rate, followed closely by other GPT‑5 and Claude Sonnet 4.5 variants. The tight clustering of top performers highlights active competition and suggests that no single model currently dominates the task space. With Harbor’s scalable infrastructure and Terminal‑Bench 2.0’s rigorously vetted tasks, the combined release sets a new benchmark for evaluating AI agents that operate in realistic terminal environments, paving the way for more standardized, reproducible research in the field.
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