--- title: "code-eval vs bigcode-evaluation-harness" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-bigcode-project-bigcode-evaluation-harness" tools: ["abacaj-code-eval", "bigcode-project-bigcode-evaluation-harness"] --- # code-eval vs bigcode-evaluation-harness *GraphCanon updated Aug 5, 2026* ## Verdict Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; 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. [code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) has 1.1k stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness). | | [code-eval](/tools/abacaj-code-eval.md) | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark. | A framework for evaluating autoregressive code generation language models. | | Stars | 431 | 1,055 | | Forks | 37 | 261 | | Open issues | 5 | 96 | | Language | Python | Python | | Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-eval](/tools/abacaj-code-eval.md) | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | | --- | --- | --- | | Days since push | 1058d | 378d | | Open issues (now) | 5 | 96 | | Owner type | User | Organization | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | ## Decision facts: code-eval - **Adopt for:** code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. ## 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. ## Choose when ### Choose code-eval if… - License: code-eval is MIT, bigcode-evaluation-harness is Apache-2.0. - Tags unique to code-eval: humaneval, wizardcoder. - When you need clear comparisons of pass rates for different LLMs using standardized tests ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, code-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, 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 NOT to use code-eval - If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences - For real-time or dynamic evaluations as this repo offers pre-computed static results only ## 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. ## Common questions ### What is the difference between code-eval and bigcode-evaluation-harness? code-eval: Run evaluation on LLMs using human-eval benchmark.. bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-eval over bigcode-evaluation-harness? Choose code-eval over bigcode-evaluation-harness when License: code-eval is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests. ### When should I choose bigcode-evaluation-harness over code-eval? Choose bigcode-evaluation-harness over code-eval when License: bigcode-evaluation-harness is Apache-2.0, code-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, 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 avoid code-eval? If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only ### 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. ### Is code-eval or bigcode-evaluation-harness more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,055 vs 431). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and bigcode-evaluation-harness open source? Yes - both are open-source projects on GitHub (code-eval: MIT, bigcode-evaluation-harness: Apache-2.0). ### Where can I find alternatives to code-eval or bigcode-evaluation-harness? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/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/abacaj-code-eval-vs-bigcode-project-bigcode-evaluation-harness.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, code-eval or bigcode-evaluation-harness? code-eval: Dormant. bigcode-evaluation-harness: 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 code-eval and bigcode-evaluation-harness? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-eval) - 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/_