All evaluations

APEX-Agents-AA Benchmark Leaderboard

Artificial Analysis' implementation of the APEX-Agents benchmark, testing AI agents on long-horizon, cross-application tasks in professional-services environments with realistic application tooling.
See example tasks

APEX-Agents is an agentic benchmark created and open-sourced by Mercor that tests long-horizon, cross-application work in professional services environments, where agents operate across files and workplace tools and are evaluated against rubrics of binary success/failure criteria using an LLM grader.
APEX-Agents-AA is Artificial Analysis' independent implementation of this benchmark, built on our open-source Stirrup Agent Harness. We evaluate 452 tasks from the public APEX-Agents dataset spanning investment banking, management consulting, and corporate law, excluding two 'worlds' which have dependencies on external APIs (Investment Banking World 244 and Investment Banking World 246).
On this page, the headline score is pass@1 success rate: the share of tasks where a model fully satisfies the grading rubric, rather than the mean rubric score across criteria.

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

APEX-Agents

Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman, Marco Burstein, Julien Benchek, David Ostrofsky, Anirudh Ravichandran, Debnil Sur, Neel Venugopal, Alannah Hsia, Isaac Robinson, Calix Huang, Olivia Varones, Daniyal Khan, Michael Haines, Austin Bridges, Jesse Boyle, Koby Twist, and .

We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate lawyers. APEX-Agents requires agents to navigate realistic work environments with files and tools. We test eight agents for the leaderboard using Pass@1. Gemini 3 Flash (Thinking=High) achieves the highest score of 24.0%, followed by GPT-5.2 (Thinking=High), Claude Opus 4.5 (Thinking=High), and Gemini 3 Pro (Thinking=High). We open source the APEX-Agents benchmark (n=480) with all prompts, rubrics, gold outputs, files, and metadata. We also open source Archipelago, our infrastructure for agent execution and evaluation.

APEX-Agents-AA Pass@1

Gemini 3.5 Flash (high) scores the highest on APEX-Agents-AA Pass@1 with a score of 47.1%, followed by GPT-5.5 (xhigh) with a score of 37.7%, and GPT-5.4 (xhigh) with a score of 33.3%

Score

APEX-Agents-AA Benchmark Leaderboard: Score

Independently benchmarked by Artificial Analysis
Reasoning models are indicated by a lightbulb icon

Token Usage

APEX-Agents-AA Benchmark Leaderboard: Output Tokens per Task

Output tokens used to run one task, broken down by reasoning and answer tokens
Reasoning models are indicated by a lightbulb icon

The average number of answer and reasoning tokens produced per benchmark task in this evaluation.

CostUpdated

APEX-Agents-AA Benchmark Leaderboard: Cost per Task

Average cost per task (USD), broken down by input, cache hit, cache write, reasoning, and answer tokens
Reasoning models are indicated by a lightbulb icon

Average cost per task in the evaluation. Costs are split by input, cache hit, cache write, reasoning, and answer token pricing where canonical token counts are available.

SpeedUpdated

APEX-Agents-AA Benchmark Leaderboard: Time per Task

Weighted average wall clock time (minutes) per task; excludes TTFT and execution time ยท Lower is better
Reasoning models are indicated by a lightbulb icon

The weighted average time (seconds) per evaluation task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the evaluation.

Score vs. Release Date

APEX-Agents-AA Benchmark Leaderboard: Score vs. Release Date

Most attractive region

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