Agents' Last Exam

Agents' Last Exam is a benchmark for how well a model acts as a general-purpose agent. The official description says: "Agents' Last Exam - frontier agentic tasks benchmark". Below we describe in simple words what benchmarks of this type generally examine.

Agents' Last Exam measures how well a model acts as a general-purpose agent. In detail, it checks:

• solving long, multi-step tasks with many intermediate decisions
• using tools correctly (browsers, terminals, APIs, calendars, other apps)
• adapting when something unexpected happens mid-task
• staying on goal over long task horizons instead of wandering off

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: Agents' Last Exam - frontier agentic tasks benchmark. Reports Pass@1 and Score (aggregate).

What is the Agents' Last Exam benchmark?

Agents' Last Exam is a benchmark for how well a model acts as a general-purpose agent. The official description says: 'Agents' Last Exam - frontier agentic tasks benchmark'. Below we describe in simple words what benchmarks of this type generally examine.

What does Agents' Last Exam test?

Agents' Last Exam mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: solving long, multi-step tasks with many intermediate decisions; using tools correctly (browsers, terminals, APIs, calendars, other apps); adapting when something unexpected happens mid-task.

How is the Agents' Last Exam score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get Agents' Last Exam data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/agents-last-exam/scores) or pull it directly from the Open LLM Radar API with any HTTP client.