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APEX–Agents

APEX–Agents is a benchmark from Mercor for evaluating whether AI agents can execute long-horizon, cross-application professional services tasks. Tasks were created by investment banking analysts, management consultants, and corporate lawyers, and require agents to navigate realistic work environments with files and tools (e.g., docs, spreadsheets, PDFs, email, chat, calendar).

  • Tasks: 480 total (160 per job category)
  • Worlds: 33 total (10 banking, 11 consulting, 12 law)
  • Rubric criteria: binary, criterion-level grading; mean ~4 criteria per task
  • Gold outputs: provided for every task
  • World assets: included (files + metadata)
  • License: CC-BY 4.0

Dataset overview

Job # Worlds Avg files / world # Tasks Avg criteria / task Avg est. hours Tasks w/ file outputs
Investment banking 10 172 160 2.93 1.36 27 (16.9%)
Law 12 161 160 4.57 2.40 20 (12.5%)
Management consulting 11 165 160 4.68 1.69 11 (6.9%)
Benchmark total 33 166 480 4.06 1.82 58 (12.1%)

Each case corresponds to a task inside a world. A “world” is a realistic project scenario created by experts. Worlds contain files and tools required to complete tasks. Web search is disabled to keep evaluations reproducible. Worlds contain applications such as: Calendar, Chat, Code Execution, Documents, File system, Mail, PDFs, Spreadsheets, Presentations. Some worlds include additional finance data applications.

A task includes:

  • Prompt: single-turn instruction given to the agent
  • Rubric: list of criteria (binary gradable statements) + grading target info
  • Gold output(s): expert-created reference output (in the requested output format)
  • Metadata: job/workflow tags, expected output type, estimated completion time, etc.
  • World context: pointers/IDs for the world plus associated files/artifacts

Evaluation

APEX–Agents uses rubric-based grading:

  • Each rubric contains multiple criteria (binary: Met / Not met).
  • There are between 1 and 10 criteria, with a mean of 4.06.
  • A judge model grades each criterion independently, using the prompt, the agent output, and relevant artifacts/changes.

Leaderboard baselines

Our paper reports results for eight agents against multiple metrics (Pass@1, Pass@8, mean score). The leaderboard uses Pass@1: the probability that a uniformly sampled task passes all criteria in a single run.

Where available, models have thinking / reasoning effort set to high.

Model Pass@1 (95% CI) Pass@8 (95% CI) Pass^8 Mean score IB analyst Pass@1 Consultant Pass@1 Lawyer Pass@1
Claude Opus 4.5 18.4% [15.5–21.3] 34.0% [29.8–38.3] 8.8% 34.8% 21.6% 13.2% 20.2%
Gemini 3 Flash 24.0% [20.7–27.3] 36.7% [32.3–41.0] 13.4% 39.5% 26.7% 19.3% 25.9%
Gemini 3 Pro 18.4% [15.7–21.1] 37.3% [32.9–41.7] 6.5% 34.1% 18.8% 12.4% 23.9%
GPT-5 18.3% [15.4–21.3] 31.0% [26.9–35.4] 7.7% 32.9% 27.3% 12.3% 15.3%
GPT-5.2 23.0% [19.8–26.2] 40.0% [35.6–44.4] 11.0% 38.7% 27.3% 22.7% 18.9%
GPT-OSS-120B 4.7% [3.3–6.1] 11.5% [8.8–14.4] 1.2% 14.5% 2.7% 3.5% 7.8%
Grok 4 15.2% [12.8–17.7] 32.9% [28.7–37.3] 4.7% 30.3% 17.0% 12.0% 16.5%
Kimi K2 Thinking 4.0% [2.9–5.2] 14.4% [11.5–17.5] 0.3% 11.5% 1.2% 2.9% 8.0%

Archipelago

Our service for executing and evalling agents is available open-source on Github. ✨View the code

How to load the dataset

from datasets import load_dataset

ds = load_dataset("mercor/apex-agents")  # replace if your org/name differs
print(ds)
print(ds["train"][0].keys())

Citation

@misc{vidgen2026apexagents,
  title        = {APEX--Agents},
  author       = {Vidgen, Bertie and Mann, Austin and Fennelly, Abby and Wright Stanly, John and Rothman, Lucas and Burstein, Marco and Benchek, Julien and Ostrofsky, David and Ravichandran, Anirudh and Sur, Debnil and Venugopal, Neel and Hsia, Alannah and Robinson, Isaac and Huang, Calix and Varones, Olivia and Khan, Daniyal and Haines, Michael and Richards, Zach and Mahapatra, Chirag and Foody, Brendan and Nitski, Osvald},
  year         = {2026},
  howpublished = {arXiv},
  url          = {https://arxiv.org/pdf/2601.14242}
}

Contact us

apex@mercor.com

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