DecodingTrust vs HELM
DecodingTrust
decodingtrust.github.io
HELM
crfm.stanford.eduSide by side
| Figure | DecodingTrust | HELM |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · LLM Evaluation Tools | 7.5 (#16) | 8.3 (#7) |
| Installation | The repository recommends cloning the project and installing it in editable mode with pip; it also describes standalone package installation. | The project instructs users to install HELM from PyPI with `pip install crfm-helm`. |
| License | The dataset and project are distributed under the CC BY-SA 4.0 license. | The repository identifies HELM as open source and links to an Apache-2.0 license. |
| Purpose | DecodingTrust is a research project for assessing trustworthiness in GPT models and helping researchers and practitioners understand their capabilities, limitations, and deployment risks. | HELM is an open-source Python framework for holistic, reproducible, and transparent evaluation of foundation models, including language and multimodal models. |
| Reproducibility | The benchmark uses timestamped GPT-3.5 and GPT-4 model versions to support consistent results and reproducibility. | The framework can be used to reproduce published model evaluation results. |
| Support | The project invites questions at [email protected]. | Users are directed to open GitHub issues for questions, which maintainers address when bandwidth is available. |
Plans
In their own words
DecodingTrust
DecodingTrust has no write-up on Specifiction yet; its figures are above.
HELM
HELM is an open-source Python framework for evaluating foundation models, including large language and multimodal systems. It supplies standardized datasets and benchmarks such as MMLU-Pro, GPQA, IFEval, and WildBench, with a unified interface for models from providers including OpenAI, Anthropic, and Google. Evaluations can examine accuracy, efficiency, bias, and toxicity, and can include custom metrics, LLM-as-a-judge checks, safety evaluations, and human review workflows. Users can configure private checkpoints or model servers, run inference locally, or send requests to an API. A web interface displays individual prompts and responses, while leaderboards compare models and benchmarks across areas such as medicine, finance, multilinguality, and world knowledge. HELM can reproduce published evaluation results and documents commands for running benchmarks, summarizing results, and starting a local server. It is free, Apache-2.0 licensed, and entered maintenance mode on June 1, 2026.