Benchmark Almanac

APEX-1

Not in model cards

Models complete expert-written work cases with source documents for four jobs: investment banking, consulting, big law and primary care. A judge model grades each response against rubrics.

The leaderboard uses a hidden set of 400 cases, 100 per job, with 8 samples per model. Mercor open-sourced 100 in-distribution cases.

In the authors’ words

We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

Abstract of The AI Productivity Index (APEX) (arXiv:2509.25721, CC0)
Made by Mercor Released Sep 2025 Tasks from real jobs Public tasks plus a private test set
Level
Expert Specialists with years of training, often over hours of work
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.

No scores here yet

We have no scores for APEX-1 yet.

See the live leaderboard on mercor.com

No lab model card reports it yet

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

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