--- title: "bigcode-evaluation-harness vs apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-hendrycks-apps" tools: ["bigcode-project-bigcode-evaluation-harness", "hendrycks-apps"] --- # bigcode-evaluation-harness vs apps *GraphCanon updated Aug 5, 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 apps if aPPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. [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. [apps](https://github.com/hendrycks/apps) has 534 stars, 70 forks, and 4 open issues, last pushed Jun 19, 2024. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [apps's repository](https://github.com/hendrycks/apps). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [apps](/tools/hendrycks-apps.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | APPS: Automated Programming Progress Standard | | Stars | 1,055 | 534 | | Forks | 261 | 70 | | Open issues | 96 | 4 | | 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. | APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT | | Categories | Evaluation & Observability | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [apps](/tools/hendrycks-apps.md) | | --- | --- | --- | | Days since push | 378d | 777d | | Open issues (now) | 96 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/hendrycks-apps/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: apps - **Adopt for:** APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, apps is MIT. - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive models, 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 apps if… - License: apps is MIT, bigcode-evaluation-harness is Apache-2.0. - Tags unique to apps: program-synthesis. - Also covers Data & Retrieval. - When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks ## 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 apps - If you solely require general datasets without a focus on coding challenges - When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation ## Common questions ### What is the difference between bigcode-evaluation-harness and apps? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. apps: APPS: Automated Programming Progress Standard. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over apps? Choose bigcode-evaluation-harness over apps when License: bigcode-evaluation-harness is Apache-2.0, apps is MIT; Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, 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 apps over bigcode-evaluation-harness? Choose apps over bigcode-evaluation-harness when License: apps is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to apps: program-synthesis; Also covers Data & Retrieval; When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks. ### 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 apps? If you solely require general datasets without a focus on coding challenges When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation ### Is bigcode-evaluation-harness or apps more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,055 vs 534). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and apps open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, apps: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or apps? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [apps alternatives](/tools/hendrycks-apps/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [apps markdown twin](/tools/hendrycks-apps/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-hendrycks-apps.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 apps? bigcode-evaluation-harness: Dormant. apps: Dormant. 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 apps? 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); [apps trust report](/tools/hendrycks-apps/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/_