The Power Sector’s AI Benchmarking Hub
How Today’s Leading LLMs Perform on
Power-Sector Questions
EPRI benchmarks leading large language models on real-world, utility-relevant questions to help the power sector understand where today’s AI systems are strong, where they fall short, and how performance evolves over time.
🟢 Live Benchmark — Updated as new models are evaluated
Multiple-choice evaluations
Open-ended short answers
Multi-run repeatability
Domain-augmented tools
Interactive Benchmark
Explore how selected models perform across EPRI benchmark datasets, including short-answer and multiple-choice questions, model-only and web-search modes, and breakdowns by difficulty or topic.
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Rotate to landscape mode for the full interactive benchmark. The chart controls and model comparisons are designed for wider screens
Four Big Insights
Hard Questions Reveal Gaps
Top models perform strongly on many standard questions, but accuracy drops on more complex, expert-level power-sector tasks.
Open-ended answers are harder
Models generally perform better on multiple-choice questions than on short-answer responses requiring reasoning and synthesis.
Web search helps, but only slightly
Search-enabled runs can improve performance, but may introduce risks from incomplete or misleading sources.
Domain-specific tools are next
The largest gains may come from pairing foundation models with utility-specific data, tools, and guardrails.
Why This Matters
Sector-Specific Evaluation For Responsible AI Adoption
AI systems are increasingly being tested for utility applications, but generic benchmarks do not fully reflect the complexity, terminology, and reliability expectations of the power sector.
EPRI's benchmarking effort provides a sector-specific way to compare model performance, identify gaps, and guide responsible adoption.
What The Benchmark Supports
- Utility relevant evaluation
Questions are grounded in the real power-sector knowledge and use cases - Documented Comparison
Models are evaluated under consistent conditions with methodology and limitations documented - Decision Support
Results help utilities, vendors, and researchers understand which AI capabilities are ready and where caution is needed
Methodology at a Glance
Documented, repeatable, and continuously updated
01
Curated benchmark sets
EPRI uses utility-relevant questions developed and reviewed with power-sector expertise.
02
Multi-phase evaluation
Models are tested across multiple-choice questions, short-answer questions, and search-enabled settings.
03
Consistent scoring
Responses are evaluated using defined scoring rules, weighted accuracy, confidence intervals, and review workflows.
04
Continuously updated
The benchmark will expand as new models, domain-augmented systems, and utility use cases are evaluated.
The Road Ahead
Applied AI and Domain-Specific Tools for Utilities
EPRI's benchmarking lays the foundation to evaluate domain-specific augmentation tools and models that deliver increased value to utilities and the broader energy ecosystem. The next phase of benchmarking will focus on real-world pilots with member utilities that integrate AI assistants into operational workflows. These pilots will measure accuracy, trust, and real operational impact.
Stay tuned for more on EPRI.AI, a retrieval-augmented assistant built on EPRI’s proprietary research library, and the Energy DSM, a domain-specific LLM trained on power-sector datasets. Comparing these tools alongside general-purpose LLMs will help determine where domain-tuned solutions offer measurable benefits.