HELM

HELM is a free, open-source Python framework for evaluating foundation models, including large language and multimodal models. It supplies standardized datasets and benchmarks such as MMLU-Pro, GPQA, IFEval, and WildBench, and provides a unified interface to models from providers including OpenAI, Anthropic, and Google. Users can also configure private models, such as a local checkpoint or a model server on a private network, and run inference locally or through an API. Its metrics cover accuracy along with efficiency, bias, and toxicity. HELM includes a web interface for inspecting prompts and responses and leaderboards for comparing models and benchmarks, including Capabilities, Safety, and VHELM. The project documents installing the framework from PyPI with `pip install crfm-helm`, running benchmarks, summarizing results, and starting a local web server. It can also reproduce published model evaluation results. HELM is distributed under an Apache-2.0 license and is self-hosted. The project entered maintenance mode on June 1, 2026; its maintainers say there will be no new features or leaderboard evaluations. Scenarios and models may break when external APIs change or providers retire models.

HELM offers broad evaluation workflows, configurable models, and tools for inspecting results in a self-hosted framework. Its maintenance status and potential compatibility changes are central considerations for a new deployment.

HELM suits teams and researchers who want an open-source framework for evaluating foundation models and can run it themselves. It may also suit users who need to inspect prompts and compare benchmark results.

HELM is a free, open-source Python framework for evaluating language and multimodal foundation models. It is best suited to teams that need repeatable comparisons across public providers and private deployments. Its evaluation and inspection tools are substantial, but maintenance mode makes it a risky foundation for workflows that need continuing updates.

Overview

HELM combines model access, benchmark runs, metrics and result inspection in a framework teams can run themselves. Its standardized datasets include MMLU-Pro, GPQA, IFEval and WildBench, and a shared interface connects to providers such as OpenAI, Anthropic and Google. Teams can also configure a local checkpoint or a model server on a private network, then run inference locally or send requests to an API.

The documented workflow covers running benchmarks, summarizing results and starting a local web server. HELM can reproduce published evaluation results, which makes it useful for repeatable research and structured model comparisons rather than one-off prompt checks. Its scope is deliberately incomplete, however, so teams may need to add scenarios, metrics or models to cover their use case.

Key features

• Standardized benchmarks and leaderboards: HELM includes benchmarks such as MMLU-Pro, GPQA, IFEval and WildBench, with official leaderboards for Capabilities, Safety and VHELM. It also maintains coverage across areas including medicine, finance, multilinguality and world knowledge. This offers a broader basis for comparison than accuracy alone, but teams should confirm the scenarios match their evaluation goals.
• Metrics beyond accuracy: Efficiency, bias and toxicity measures help teams examine operational and safety dimensions alongside task performance. HELM also supports safety evaluations, custom metrics, LLM-as-a-judge and human review workflows, giving evaluation teams room to shape how they assess results.
• Prompt-level inspection: A web UI exposes individual prompts and responses, while the web leaderboard compares results across models and benchmarks. That makes it easier to investigate what sits behind an aggregate score instead of treating the leaderboard as the whole evaluation.
• Flexible model setup: Teams can use provider APIs or configure private models, including local checkpoints and private-network model servers. This flexibility suits evaluations that cannot rely solely on hosted providers, though model and scenario compatibility can change as external APIs and provider offerings change.
• Reproducible runs: The framework is designed to reproduce published model evaluations and includes commands for benchmark execution, result summaries and a local web server. The payoff is a more traceable comparison process; the tradeoff is that incomplete coverage may require team contributions.

Pricing

HELM is free: its HELM plan costs nothing and provides the open-source Python framework. There are no paid plans, free trial or paid tier to unlock. The absence of a subscription makes it accessible for research groups and engineering teams able to manage their own deployment, but it does not come with a paid support commitment.

Installation is through PyPI with pip install crfm-helm. HELM is self-hosted, so teams should plan to run and maintain the framework in their own environment rather than expect a hosted service. The project identifies the repository as open source under Apache-2.0.

Platforms

HELM supports API, self-hosted and web use. Its Python installation and local web server fit teams comfortable running software in their own environment; the web interface is for inspecting prompts and responses and comparing evaluation results, not a substitute for the underlying deployment.

Who it's for

HELM is a strong fit for research and engineering teams that value reproducible model evaluation, need to compare multiple providers, or must include privately deployed models. Its metrics and inspection tools also suit teams that need to look beyond headline accuracy and examine safety or individual responses.

It is a weaker choice for teams seeking an actively expanding evaluation suite, turnkey hosted operations or dependable support for fast-changing provider APIs. HELM entered maintenance mode on June 1, 2026; no new features or leaderboard evaluations will be added. Maintainers address GitHub issues when bandwidth allows, and recommend testing scenarios and models for the intended use because external changes can break compatibility.

Pros and cons

• Pros: Free and Apache-2.0 licensed, with no paid-plan limits; reproducible benchmark workflows; access to major providers and private models; metrics beyond accuracy; prompt-level inspection and comparative leaderboards.
• Cons: Maintenance mode means no new features or leaderboard evaluations; external API and model changes can break scenarios; intentionally incomplete coverage can require custom additions; support depends on maintainer bandwidth.

Alternatives

Choose Vellum instead if a freemium option with Android, iOS, desktop and web platforms is a better fit; its Free plan includes initial tasks and pay-as-you-go credits, while Mighty costs $30 a month.

Weights & Biases is worth considering if you want a freemium platform with a free plan capped at 5 model seats, 5 GB/mo storage and 1 GB/mo Weave data ingestion, plus a free trial.

Consider Opik if you prefer open-source core observability and evaluation features that can be downloaded and run locally, alongside a freemium cloud option.

Braintrust offers a freemium Starter plan with 1 GB processed data, 10,000 scores and 14-day retention, which may suit teams that want a hosted alternative with defined usage limits.

Confident AI may fit teams looking for a freemium hosted option with a free tier limited to 2 user seats, 1 project, 5 test runs per week and 1 GB-month of trace spans.

DeepEval is another free, Apache-2.0 open-source framework, with local and CI/CD test-runner support; choose it when those execution options suit your workflow better.

Evidently AI provides a free, Apache-2.0 open-source framework and can be an alternative for teams seeking open-source tooling across API, local and web platforms.

Galileo is a freemium alternative with a Pro plan at $100 a month, billed yearly, including 50,000 traces per month, standard RBAC, advanced analytics and dedicated Slack support.

For a broader comparison, browse AI LLM Evaluation Tools or LLM Evaluation Tools.

Verdict

HELM is a compelling choice for technically equipped teams that want free, reproducible foundation-model evaluations, flexible model access and transparent inspection of results. Its central advantage is the breadth of its evaluation workflow without a subscription. Choose another tool if ongoing benchmark development, compatibility resilience or more responsive support matters more than that control and openness.