A benchmark of expert-level academic questions to assess AI capabilities

Abstract

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve more than 90% accuracy on popular benchmarks such as Measuring Massive Multitask Language Understanding¹, limiting informed measurement of state-of-the-art LLM capabilities. Here, in response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be an expert-level closed-ended academic benchmark with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable but cannot be quickly answered by internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a marked gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai .

Fig. 1. Performance of frontier LLMs on popular benchmarks and HLE.

Compared with the saturation of other popular capability benchmarks, HLE accuracy remains low across several frontier models, demonstrating its effectiveness for measuring advanced, closed-ended, academic capabilities.

Fig. 2. Distribution of HLE questions across categories.

HLE consists of 2,500 exam questions in over a hundred subjects, grouped into eight high-level categories.

Fig. 3. Example questions from HLE.

Samples of the diverse and challenging questions submitted to HLE.

Fig. 4. HLE dataset creation pipeline.

We accept questions that make frontier LLMs fail, then iteratively refine them with the help of expert peer reviewers. Each question is then manually approved by organizers or expert reviewers trained by organizers. A private held-out set is kept apart from the public set to assess model overfitting and gaming on the public benchmark.

Fig. 5. Accuracy compared with reasoning token budget.

Accuracy binned by the total number of generated output tokens, showing a log-linear increase in accuracy peaking around 2¹⁴ tokens before reversing.

Extended Data Fig. 1. Example of a structured response using an LLM judge.

Exact-match answers in HLE sometimes require several reasoning steps to compare the AI’s final answer with the correct answer; therefore, a capable LLM judge with reasoning capabilities is necessary.