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Terminal-Bench 2.0 tests whether AI agents can chain together the kind of multi-step, unsupervised terminal work a developer does in a day — compiling code, training models, setting up servers. Stanford's 2026 AI Index reports agent accuracy on it nearly quadrupled in a year, reaching 77.3% by early 2026, up from 20% in February 2025
Verified 2026-09-02
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More on Models
- AI models can now win gold at the International Mathematical Olympiad, yet Stanford's 2026 AI Index found they still fumble a task most children master early. Across ClockBench's 180 clock designs and 720 questions, the top model, GPT-5.4 High, read analog clocks correctly only 50.6% of the time, versus 90.1% for humans
- Stanford's 2026 AI Index tracks how AI models perform on GPQA Diamond, a set of PhD-level chemistry and physics questions designed to resist Google searches. Mean model accuracy on the benchmark reached 93% in 2025, compared with an expert human validator baseline of 81.2% -- AI models now beat PhD-level experts by 12 points on GPQA Diamond
- Frontier AI models are catching up fast to benchmarks meant to resist them. A year ago they scored under 10% on Humanity's Last Exam, a 2,700-question test designed to be hard for AI and favorable to human experts. Now accuracy has reached 38.3% on Humanity's Last Exam, up from under 10% a year earlier