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reasoning benchmark

Agents' Last Exam

Agents' Last Exam is a challenging benchmark for AI agents on hard, long-horizon tasks that test sustained reasoning, planning, and tool use, reported with and without tool access.

Updated Aug 11, 2026

Models5
Model coverage5
MetricScore
EvidenceB

On this page

  • Ranking
  • Distribution
  • Highlights
  • About
  • FAQ

Agents' Last Exam Ranking

Higher score ranks better on this benchmark.

5 rows
Columns

Show columns

01OPGPT-5.6 SolOpenAI52.7%100.0%5CAug 11, 2026
02OPGPT-5.6 TerraOpenAI50.4%75.0%5CAug 11, 2026
03OPGPT-5.6 LunaOpenAI50.3%50.0%5CAug 11, 2026
04BYSeed 2.1 ProByteDance41.4%25.0%5CAug 11, 2026
05DEDeepSeek-V4-Flash-0731DeepSeek25.2%0.0%5CAug 11, 2026

Agents' Last Exam Score Distribution

A closer view of the leading scores on this benchmark.

Agents' Last Exam

Agents' Last Exam Highlights

The leading models and scores on this benchmark.

Rank #1GPT-5.6 Sol52.7%Rank #2GPT-5.6 Terra50.4%Rank #3GPT-5.6 Luna50.3%Rank #4Seed 2.1 Pro41.4%

What is Agents' Last Exam?

What Agents' Last Exam measures and how its scores work.

Agents' Last Exam is a challenging benchmark for AI agents on hard, long-horizon tasks that test sustained reasoning, planning, and tool use, reported with and without tool access.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
Agents' Last Exam
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
agents-last-exam|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about Agents' Last Exam.

Which model scores highest on Agents' Last Exam?

GPT-5.6 Sol is currently ranked first with 52.7%.

What does Agents' Last Exam measure?

Agents' Last Exam is a challenging benchmark for AI agents on hard, long-horizon tasks that test sustained reasoning, planning, and tool use, reported with and without tool access.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

5 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.