--- title: "bigcode-evaluation-harness vs human-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-openai-human-eval" tools: ["bigcode-project-bigcode-evaluation-harness", "openai-human-eval"] --- # bigcode-evaluation-harness vs human-eval *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 human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. [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. [human-eval](https://github.com/openai/human-eval) has 3.3k stars, 452 forks, and 44 open issues, last pushed Jan 17, 2025. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [human-eval's repository](https://github.com/openai/human-eval). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Evaluating Large Language Models Trained on Code | | Stars | 1,055 | 3,331 | | Forks | 261 | 452 | | Open issues | 96 | 44 | | 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. | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT | | 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) | [human-eval](/tools/openai-human-eval.md) | | --- | --- | --- | | Days since push | 378d | 564d | | Open issues (now) | 96 | 44 | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/openai-human-eval/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: human-eval - **Hosting:** self hosted - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process. - **Pricing:** freemium - The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs. - **Adopt for:** human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, human-eval 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, code generation, docker. - 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 human-eval if… - License: human-eval is MIT, bigcode-evaluation-harness is Apache-2.0. - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process. - Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.. - Tags unique to human-eval: code evaluation, large language models. - When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models. ## 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 human-eval - If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems. - When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies. ## Common questions ### What is the difference between bigcode-evaluation-harness and human-eval? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. human-eval: Evaluating Large Language Models Trained on Code. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over human-eval? Choose bigcode-evaluation-harness over human-eval when License: bigcode-evaluation-harness is Apache-2.0, human-eval 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, code generation, docker; 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 human-eval over bigcode-evaluation-harness? Choose human-eval over bigcode-evaluation-harness when License: human-eval is MIT, bigcode-evaluation-harness is Apache-2.0; This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process; Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.; Tags unique to human-eval: code evaluation, large language models; When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models. ### 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 human-eval? If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems. When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies. ### Is bigcode-evaluation-harness or human-eval more popular on GitHub? human-eval has more GitHub stars (3,331 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and human-eval open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, human-eval: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or human-eval? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [human-eval alternatives](/tools/openai-human-eval/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [human-eval markdown twin](/tools/openai-human-eval/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-openai-human-eval.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 human-eval? bigcode-evaluation-harness: Dormant. human-eval: 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 human-eval? 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); [human-eval trust report](/tools/openai-human-eval/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/_