Openai introduces GDPVAL: a new evaluation suite that measures AI based on real-world valuable tasks

Introduction to Openai GDPVALThis is a new evaluation suite designed to measure how AI models perform real-world valuable tasks in 44 careers in the U.S. field. Unlike academic benchmarks, GDPVAL’s center on real deliverables – performances, spreadsheets, profiles, CAD artifacts, audio/video – was earned by professional experts through blind pairwise comparisons. Openai also released a “golden” subset of 220 missions and an experimental automation extension held at evals.openai.com.

From benchmark to account: How GDPVAL builds tasks

GDPVAL aggregates 1,320 missions Average from industry professionals 14 years experience. Tasks are mapped to o* net work activities and include multi-modal file processing (document, slideshow, images, audio, video, spreadsheet, CAD), with up to dozens of reference files per task. The gold subset provides public tips and references; due to subjectivity and format requirements, the main ratings still rely on expert pairwise judgments.

What is the data saying: Models and experts

In the gold subset, Border model approaches expert quality In most tasks under blind expert review, model progress tends roughly linearly in the distribution. Reported model vs human Win-win rate Almost parity, error profile clusters of top-level models revolve around instruction following, formatting, data usage, and hallucination. Improve reasoning and stronger scaffolding (e.g., format checking, self-checking artifact rendering) produces predictable benefits.

Time-Cost Mathematics: The Return of AI

GDPVAL operation Program Analysis Compare the sole person to model assisted workflow with expert review. It quantifies (i) wage-based costs, (ii) reviewer time/cost, (iii) model latency and API costs, and (iv) empirically observed winning rates. Results show potential Time/cost reduction For many task courses, once the audit overhead is included.

Automatic judgment: a useful proxy, not Oracle

For a subset of gold, Automated paired classifiers show About 66% of the agreement 5 percentage points (~71%) consistent with humans and humans (~71%). It is positioned as a fast iterative accessibility proxy, rather than a replacement for expert review.

Why is this not another benchmark

• Career Broadness: Spreading the top GDP sector and a lot of net work activities, not just narrow domains.
• Deliverable Realism: Multi-file, multi-mode input/output stress structure, format and data processing.
• Mobile ceiling: use Human preference win rate Oppose expert deliverables, as the model improves, the foundation can be lowered again.

Boundary conditions: Where GDPVAL is not met

GDPVAL-V0 target Computer-mediated knowledge work. Sports labor, long distance interactions and organization-specific tools are not scoped. The task is One sound, precisely specified;Ablation indicates a decrease in performance. Build and grading are Resource-intensivea classifier that inspires automation (record to limits) and future expansions.

Suitable for stacks: How GDPVAL complements other Evals

GDPVAL enhances existing Openai Evals Career, multi-mode, document-centric Tasks and reports on human preference outcomes, time/cost analysis and the ablation of inference work and agent scaffolding. V0 is Version And expect to expand coverage and realism over time.

Summary

GDPVAL formally evaluates economically relevant knowledge work by pairing expert-built tasks with blind human preference judgments and accessible automated hierarchical pairing. The framework quantifies model quality and practical time/cost tradeoffs while revealing the impact of failure modes and scaffolding and reasoning efforts. The range is still V0 (computer-mediated, one-time task with expert review), but it establishes reproducible benchmarks for tracking real-world capability growth across careers.

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Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex data sets into actionable insights.