Benchmark RadarAI BENCHMARK PROFILE
OmegaUse-OfficeVal
Evaluates LLM agents on long-horizon office-suite tasks from 100 practitioner-derived scenarios, scoring deliverable quality through code-based verifiers and comparing performance against economic signals of human labor time and price.
- Released
- 2026-07-29
- Readiness
- Runnable
- Primary field
- General AI
Why it matters
Existing agent benchmarks rarely consider cost-effectiveness for real office workflows. This benchmark provides a way to compare agent output quality relative to human labor costs, supporting value-weighted evaluation of office automation.
Motivation
Large language model (LLM) agents are increasingly expected to assist users in completing tasks.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.