All evaluations

EnterpriseOps-Gym-AA Benchmark Leaderboard

Artificial Analysis' independent implementation of ServiceNow's EnterpriseOps-Gym, an agentic benchmark testing whether LLM agents can complete stateful, multi-step enterprise workflows across eight business domains via live tool use, graded on the final state of the underlying databases.
See example tasks

EnterpriseOps-Gym is an agentic benchmark developed by ServiceNow that measures whether LLM agents can complete stateful, multi-step enterprise workflows. Each task takes place in a single business domain: the everyday collaboration tools email, calendar, teams, and drive, or the core business systems customer service, HR, and IT service management. The eighth domain, hybrid, is the exception, with tasks that span several of these systems at once. In each case the agent is dropped into a live sandbox and asked to carry out real operational work through tool calls.
Tasks are graded on the final state of the underlying databases rather than the conversation transcript, so scores reflect whether the agent actually accomplished the work. No partial credit is given: a task counts as a success only when every verifier over the resulting state passes.
The EnterpriseOps-Gym-AA implementation is run with our Stirrup agent harness on ServiceNow's dataset, in the benchmark's oracle tool mode with 3 repeats per task. The headline score is the strict task success rate.

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

Publication

View on arXiv

EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings

Shiva Krishna Reddy Malay, Shravan Nayak, Jishnu Sethumadhavan Nair, Sagar Davasam, Aman Tiwari, Sathwik Tejaswi Madhusudhan, Sridhar Krishna Nemala, Srinivas Sunkara, Sai Rajeswar.

We introduce EnterpriseOps-Gym, a benchmark designed to evaluate agentic planning in realistic enterprise settings. EnterpriseOps-Gym features a containerized sandbox with 164 database tables and 512 functional tools to mimic real-world search friction. Within this environment, agents are evaluated on 1,150 expert-curated tasks across eight mission-critical verticals (including Customer Service, HR, and IT).

EnterpriseOps-Gym-AA Task success rate

Claude Fable 5 (Adaptive Reasoning, Max Effort, Opus 4.8 Fallback) scores the highest on EnterpriseOps-Gym-AA Task success rate with a score of 51.1%, followed by Gemini 3.5 Flash (high) with a score of 50.1%, and GPT-5.5 (xhigh) with a score of 46.6%

Score

EnterpriseOps-Gym-AA: Score

Overall task success rate across all eight enterprise domains (oracle tool mode) · Benchmark developed by ServiceNow Research · Independently benchmarked by Artificial Analysis
Reasoning models are indicated by a lightbulb icon

EnterpriseOps-Gym-AA: Success Rate by Domain

Strict task success rate by domain · Scores are normalized per domain across the models currently selected, where green is the highest-scoring selected model for that domain and red is the lowest. Colors rescale when the selection changes.
Reasoning models are indicated by a lightbulb icon

EnterpriseOps-Gym-AA: Average Turns per Task

Average number of agent turns used per task · 100-turn cap · Oracle tool mode
Reasoning models are indicated by a lightbulb icon

Token Usage

EnterpriseOps-Gym-AA: 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.

Cost

EnterpriseOps-Gym-AA: 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.

Speed

EnterpriseOps-Gym-AA: Time per Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · 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

EnterpriseOps-Gym-AA: Score vs. Release Date

Most attractive region

Explore Evaluations

Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index

A composite benchmark aggregating nine challenging evaluations to provide a holistic measure of AI capabilities across mathematics, science, coding, and reasoning.

Artificial Analysis Openness IndexArtificial Analysis Openness Index

A composite measure providing an industry standard to communicate model openness for users and developers.

AA-Briefcase: Agentic Knowledge Work BenchmarkAA-Briefcase: Agentic Knowledge Work Benchmark

A private evaluation developed by Artificial Analysis for frontier agentic capability in long-horizon knowledge work, testing agents on realistic business workflows that require deliverables such as spreadsheets, presentations, and memos.

GDPval-AA v2 LeaderboardGDPval-AA v2 Leaderboard

GDPval-AA v2 is Artificial Analysis' evaluation framework for OpenAI's GDPval dataset. It tests AI models on real-world tasks across 44 occupations and 9 major industries. Models are given shell access and web browsing capabilities in an agentic loop via Stirrup to solve tasks, with Elo ratings derived from blind pairwise comparisons.

APEX-Agents-AA Benchmark LeaderboardAPEX-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.

AutomationBench-AA: Agentic SaaS Workflow BenchmarkAutomationBench-AA: Agentic SaaS Workflow Benchmark

A benchmark measuring agentic task completion across simulated SaaS application environments, scoring the share of each task's objectives completed without guardrail violations.

Harvey LAB-AA Benchmark LeaderboardHarvey LAB-AA Benchmark Leaderboard

Artificial Analysis' implementation of Harvey's Legal Agent Benchmark (LAB), testing AI agents on real-world legal work from Harvey's dataset of 120 private tasks spanning 24 legal practice areas. The agent reads case documents in a sandbox and produces legal deliverables (e.g., memos, disclosure schedules, deposition summaries), graded criterion-by-criterion by a single LLM rubric judge.

EnterpriseOps-Gym-AA Benchmark LeaderboardEnterpriseOps-Gym-AA Benchmark Leaderboard

Artificial Analysis' independent implementation of ServiceNow's EnterpriseOps-Gym, an agentic benchmark testing whether LLM agents can complete stateful, multi-step enterprise workflows across eight business domains via live tool use, graded on the final state of the underlying databases.

𝜏³-Banking Benchmark Leaderboard𝜏³-Banking Benchmark Leaderboard

A fintech customer-support benchmark from the 𝜏-Knowledge framework that tests whether agents can navigate a large unstructured knowledge base and execute multi-step tool calls to resolve realistic banking workflows.

Terminal-Bench v2.1 Benchmark LeaderboardTerminal-Bench v2.1 Benchmark Leaderboard

A verified refresh of Terminal-Bench v2.0 — 89 curated tasks across software engineering, system administration, data processing, model training, and security, with environment and instruction fixes so scores reflect agent capability rather than environment gaps.

Artificial Analysis Long Context Reasoning Benchmark LeaderboardArtificial Analysis Long Context Reasoning Benchmark Leaderboard

A challenging benchmark measuring language models' ability to extract, reason about, and synthesize information from long-form documents ranging from 10k to 100k tokens (measured using the cl100k_base tokenizer).

AA-Omniscience: Knowledge and Hallucination BenchmarkAA-Omniscience: Knowledge and Hallucination Benchmark

A benchmark measuring factual recall and hallucination across various economically relevant domains.

SciCode Benchmark LeaderboardSciCode Benchmark Leaderboard

A scientist-curated coding benchmark featuring 288 test set subproblems from 80 laboratory problems across 16 scientific disciplines.

Humanity's Last Exam Benchmark LeaderboardHumanity's Last Exam Benchmark Leaderboard

A frontier-level benchmark with 2,500 expert-vetted questions across mathematics, sciences, and humanities, designed to be the final closed-ended academic evaluation.

CritPt Benchmark LeaderboardCritPt Benchmark Leaderboard

A benchmark designed to test LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges.

GPQA Diamond Benchmark Leaderboard

The most challenging 198 questions from GPQA, where PhD experts achieve 65% accuracy but skilled non-experts only reach 34% despite web access.

ITBench-AA Benchmark LeaderboardITBench-AA Benchmark Leaderboard

Artificial Analysis' implementation of IBM's ITBench benchmark, testing AI agents on Kubernetes incident root-cause analysis from offline incident snapshots. The agent inspects alerts, events, traces, and topology and identifies the contributing-factor entities (deployments, pods, namespaces, network policies, etc.) responsible for the failure.

MMMU-Pro Benchmark LeaderboardMMMU-Pro Benchmark Leaderboard

An enhanced MMMU benchmark that eliminates shortcuts and guessing strategies to more rigorously test multimodal models across 30 academic disciplines.

IFBench Benchmark LeaderboardIFBench Benchmark Leaderboard

A benchmark evaluating precise instruction-following generalization on 58 diverse, verifiable out-of-domain constraints that test models' ability to follow specific output requirements.

Terminal-Bench Hard Benchmark LeaderboardTerminal-Bench Hard Benchmark Leaderboard

An agentic benchmark evaluating AI capabilities in terminal environments through software engineering, system administration, and data processing tasks.

𝜏²-Bench Telecom Benchmark Leaderboard𝜏²-Bench Telecom Benchmark Leaderboard

A dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.

MMLU-Pro Benchmark LeaderboardMMLU-Pro Benchmark Leaderboard

An enhanced version of MMLU with 12,000 graduate-level questions across 14 subject areas, featuring ten answer options and deeper reasoning requirements.

LiveCodeBench Benchmark LeaderboardLiveCodeBench Benchmark Leaderboard

A contamination-free coding benchmark that continuously harvests fresh competitive programming problems from LeetCode, AtCoder, and CodeForces, evaluating code generation, self-repair, and execution.

MATH-500 Benchmark LeaderboardMATH-500 Benchmark Leaderboard

A 500-problem subset from the MATH dataset, featuring competition-level mathematics across six domains including algebra, geometry, and number theory.

AIME 2025 Benchmark LeaderboardAIME 2025 Benchmark Leaderboard

All 30 problems from the 2025 American Invitational Mathematics Examination, testing olympiad-level mathematical reasoning with integer answers from 000-999.

Global-MMLU-Lite Benchmark LeaderboardGlobal-MMLU-Lite Benchmark Leaderboard

A lightweight, multilingual version of MMLU, designed to evaluate knowledge and reasoning skills across a diverse range of languages and cultural contexts.