Terminal‑Bench 2.0, released in early November 2025, builds on the rapid adoption of its predecessor to become the industry’s go‑to standard for measuring AI agents that operate through command‑line interfaces. The new suite addresses long‑standing pain points—unstable tasks, inconsistent specifications, and the lack of a scalable test harness—by introducing a rigorously verified task set and a dedicated runtime framework, Harbor. Together, they enable developers and researchers to assess agent performance in realistic developer environments while ensuring reproducibility and fair comparison across models.
The benchmark now contains 89 tasks, each vetted through several hours of manual review and large‑language‑model‑assisted validation. Emphasis was placed on solvability, realism, and clear specification, raising the difficulty ceiling while trimming out dependency‑heavy tasks such as the now‑refactored download‑youtube target. Despite the increased rigor, top GPT‑5 variants achieve a 49.6 % success rate, comparable to the 1.0 release but with higher task quality and reliability. The leaderboard’s tight clustering of scores reflects a healthy competitive landscape where no single agent dominates, underscoring the need for continuous improvement and robust evaluation.
Harbor, the companion runtime, extends Terminal‑Bench’s reach by providing a unified, cloud‑native framework that supports thousands of container deployments across providers like Daytona and Modal. It is agnostic to agent architecture, offering pipelines for supervised fine‑tuning and reinforcement learning, custom benchmark creation, and full integration with Terminal‑Bench 2.0. Researchers can submit agents with a simple CLI command, run multiple attempts, and contribute results to a public leaderboard. Early adopters already incorporate the benchmark into workflows for agentic reasoning, code generation, and tool use, while a forthcoming preprint details the verification methodology. Together, Terminal‑Bench 2.0 and Harbor lay the groundwork for a standardized, scalable evaluation stack that will shape the future of autonomous AI agent development. Its open‑source nature and comprehensive documentation lower the barrier for community participation, encouraging rapid iteration and shared best practices.
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