HELM review
Open-source benchmarking for standardized, safety-focused model comparisons.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
HELM is an open-source Python framework from Stanford CRFM for holistic, reproducible, and transparent evaluation of foundation models, including large language and multimodal models. It is designed for researchers, academic teams, and safety practitioners who need standardized datasets, repeatable benchmarks, and comparable results across models. Its unified interface supports models from OpenAI, Anthropic, and Google Gemini, while leaderboards help compare model and benchmark results. A local web interface provides inspection of prompts and model responses.
The evaluation layer is broad rather than limited to accuracy. HELM supports metrics covering accuracy, efficiency, bias, toxicity, and other dimensions, alongside dedicated safety benchmark evaluations. Teams can configure custom benchmarks with prompt templates, datasets, and metric lists, then use human critique or LLM critique evaluations. This combination suits projects that need both standardized academic comparisons and tailored evaluation workflows, especially when quality and safety need to be reviewed together.
HELM is deployed through a self-hosted setup, so organizations need to manage the local environment and evaluation runs themselves. Its open-source model supports local inspection and customization, but the product is not positioned as a broad hosted integration layer. HELM entered maintenance mode on June 1, 2026, while the repository remains available with documented installation and releases. Choose HELM when reproducibility, transparent benchmarking, safety metrics, and configurable evaluation matter most. Teams seeking a managed service, wider integrations, or an actively expanding product may prefer another tool.
HELM pros and cons
- Where it wins
- Standardized datasets, benchmarks, and broad quality and safety metrics
- Compares models from OpenAI, Anthropic, and Google Gemini
- Supports custom benchmarks, human critique, and LLM critique
- Where it doesn't
- Self-hosted deployment requires local installation and management
- Integration coverage is limited to listed model providers
- Entered maintenance mode on June 1, 2026
HELM fact sheet, pricing and score →
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