Berkeley's Agents' Last Exam: GPT-5.5 Leads at 24%, Every Frontier Model Scores 0% on the Hardest Tier
UC Berkeley’s Center for Responsible, Decentralized Intelligence has published Agents’ Last Exam — a benchmark designed to measure whether AI agents can complete economically meaningful professional work, not just pass tests.
The results are direct: the best frontier agents pass about 24% of tasks overall. On the hardest-tier tasks, every frontier agent tested scores 0%.
What ALE Measures
ALE covers 1,490+ long-horizon tasks across 55 non-physical occupational domains, drawn from the U.S. federal O*NET / SOC 2018 occupational taxonomy. Each task is graded by code-verified outcomes with no human judges. An agent must produce deliverables that a domain expert has already validated against objective success criteria.
The benchmark is organized in tiers. Standard tasks measure whether an agent can execute a realistic professional workflow from start to finish. The “Last Exam” tier represents the hardest professional work in each domain — the kind of task where a senior practitioner earns their salary.
Led by Dawn Song, who also built MMLU, MATH, and CyberGym, ALE launched with 1,490 tasks and is designed as a living benchmark scaling toward 5,000. It was built with 250+ domain experts across more than 100 institutions.
Leaderboard at Launch
| Agent | Pass Rate (Overall) |
|---|---|
| GPT-5.5 (Codex harness) | 24.0% |
| Cursor Composer 2.5 | ~22% |
| Claude Fable 5 | 22.0% |
| Claude Opus 4.8 | Low single digits |
On the hardest “Last Exam” tier, the average full pass rate across all configurations is 2.6%. Claude Opus 4.8 and every other frontier agent tested post 0.0%.
Cost per completed task:
- Claude Fable 5: ~$15.70
- GPT-5.5: ~$3.80
- Cursor Composer 2.5: ~$1.33
The SWE-Bench Gap
ALE’s design deliberately separates from SWE-bench. SWE-bench tests whether an agent can locate and fix a specific code issue in an existing repo. ALE tests whether an agent can execute a multi-week professional workflow: a complete regulatory filing, a functional analytics dashboard built from raw data, a resolved insurance claim across multiple systems.
GPT-5.5 leads ALE despite Fable 5 leading SWE-bench Verified (95.0% versus GPT-5.5’s 88.7%). Researchers attribute GPT-5.5’s edge to better performance under context fatigue across very long workflows and more reliable recovery when an intermediate step fails. Fable 5 is more accurate in the early phases of a task but loses ground in multi-step error recovery.
The cost gap is the sharper finding. GPT-5.5 delivers comparable ALE performance at roughly one-quarter the per-task cost of Fable 5. For enterprise deployments running parallel agent tasks at scale, that differential translates to millions per month.
Saturation-Resistant Design
Most agent benchmarks saturate within a year. ALE is built to avoid that. Tasks are continuously onboarded, grading is code-verified rather than judge-based, and the hardest tier is anchored to professional work that humans genuinely struggle to automate.
The 0% on the Last Exam tier is not measurement error. It is the stated intent: the work most valuable to employers is still out of reach for current frontier agents. ALE puts a number on where that ceiling sits today.