--- title: "bigcode-evaluation-harness vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-stanford-crfm-helm" tools: ["bigcode-project-bigcode-evaluation-harness", "stanford-crfm-helm"] --- # bigcode-evaluation-harness vs helm *GraphCanon updated Aug 7, 2026* ## Verdict Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick helm if helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 406 forks, and 90 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [helm's repository](https://github.com/stanford-crfm/helm). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 1,055 | 2,873 | | Forks | 261 | 406 | | Open issues | 96 | 90 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 378d | 5d | | Open issues (now) | 96 | 90 | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: helm - **Adopt for:** Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. ## Choose when ### Choose bigcode-evaluation-harness if… - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python. - bigcode-evaluation-harness ships Docker support for self-hosted deployment. - When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### Choose helm if… - Tags unique to helm: evaluation, foundation-models, framework, language-models. - When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. - More GitHub stars (2.9k vs 1.1k) - visibility, not fit. ## When NOT to use bigcode-evaluation-harness - When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. - If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ## When NOT to use helm - Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. - If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ## Common questions ### What is the difference between bigcode-evaluation-harness and helm? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over helm? Choose bigcode-evaluation-harness over helm when Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### When should I choose helm over bigcode-evaluation-harness? Choose helm over bigcode-evaluation-harness when Tags unique to helm: evaluation, foundation-models, framework, language-models; When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way; More GitHub stars (2.9k vs 1.1k) - visibility, not fit. ### When should I avoid bigcode-evaluation-harness? When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ### When should I avoid helm? Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ### Is bigcode-evaluation-harness or helm more popular on GitHub? helm has more GitHub stars (2,873 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and helm open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, helm: Apache-2.0). ### Where can I find alternatives to bigcode-evaluation-harness or helm? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [helm markdown twin](/tools/stanford-crfm-helm/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/bigcode-project-bigcode-evaluation-harness-vs-stanford-crfm-helm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or helm? bigcode-evaluation-harness: Dormant. helm: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for bigcode-evaluation-harness and helm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [helm trust report](/tools/stanford-crfm-helm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_