--- title: "bigcode-evaluation-harness vs ACLUE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-isen-zhang-aclue" tools: ["bigcode-project-bigcode-evaluation-harness", "isen-zhang-aclue"] --- # bigcode-evaluation-harness vs ACLUE *GraphCanon updated Aug 6, 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 ACLUE if aCLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge. [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. [ACLUE](https://github.com/isen-zhang/ACLUE) has 34 stars, 0 forks, and 0 open issues, last pushed Mar 20, 2024. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [ACLUE's repository](https://github.com/isen-zhang/ACLUE). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [ACLUE](/tools/isen-zhang-aclue.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | Evaluation Benchmark for Ancient Chinese Language Comprehension | | Stars | 1,055 | 34 | | Forks | 261 | 0 | | Open issues | 96 | 0 | | 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. | ACLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT License: Permissive open-source license allowing free use and modification of the software, including commercially. | | 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) | [ACLUE](/tools/isen-zhang-aclue.md) | | --- | --- | --- | | Days since push | 378d | 868d | | Open issues (now) | 96 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/isen-zhang-aclue/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: ACLUE - **Adopt for:** ACLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge. - **License detail:** MIT License: Permissive open-source license allowing free use and modification of the software, including commercially. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, ACLUE 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. ### Choose ACLUE if… - License: ACLUE is MIT, bigcode-evaluation-harness is Apache-2.0. - Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks. - When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 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 ACLUE - For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension - When the focus is strictly on contemporary texts without a need for historical language understanding capabilities ## Common questions ### What is the difference between bigcode-evaluation-harness and ACLUE? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. ACLUE: Evaluation Benchmark for Ancient Chinese Language Comprehension. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over ACLUE? Choose bigcode-evaluation-harness over ACLUE when License: bigcode-evaluation-harness is Apache-2.0, ACLUE 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 choose ACLUE over bigcode-evaluation-harness? Choose ACLUE over bigcode-evaluation-harness when License: ACLUE is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks; When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 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 ACLUE? For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension When the focus is strictly on contemporary texts without a need for historical language understanding capabilities ### Is bigcode-evaluation-harness or ACLUE more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,055 vs 34). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and ACLUE open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, ACLUE: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or ACLUE? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [ACLUE alternatives](/tools/isen-zhang-aclue/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [ACLUE markdown twin](/tools/isen-zhang-aclue/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-isen-zhang-aclue.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 ACLUE? bigcode-evaluation-harness: Dormant. ACLUE: 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 ACLUE? 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); [ACLUE trust report](/tools/isen-zhang-aclue/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/_