--- title: "bigcode-evaluation-harness vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-huybery-awesome-code-llm" tools: ["bigcode-project-bigcode-evaluation-harness", "huybery-awesome-code-llm"] --- # bigcode-evaluation-harness vs Awesome-Code-LLM *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 Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. [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. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) has 1.3k stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 1,055 | 1,291 | | Forks | 261 | 74 | | Open issues | 96 | 4 | | Language | 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. | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Days since push | 378d | 604d | | Open issues (now) | 96 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/huybery-awesome-code-llm/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: Awesome-Code-LLM - **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs. - **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. - **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, Awesome-Code-LLM 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 Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, bigcode-evaluation-harness is Apache-2.0. - Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.. - Tags unique to Awesome-Code-LLM: awesome, large language models. - Also covers LLM Frameworks. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ## 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 Awesome-Code-LLM - When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. - If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. - In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ## Common questions ### What is the difference between bigcode-evaluation-harness and Awesome-Code-LLM? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over Awesome-Code-LLM? Choose bigcode-evaluation-harness over Awesome-Code-LLM when License: bigcode-evaluation-harness is Apache-2.0, Awesome-Code-LLM 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 Awesome-Code-LLM over bigcode-evaluation-harness? Choose Awesome-Code-LLM over bigcode-evaluation-harness when License: Awesome-Code-LLM is MIT, bigcode-evaluation-harness is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, large language models; Also covers LLM Frameworks; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### 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 Awesome-Code-LLM? When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering ### Is bigcode-evaluation-harness or Awesome-Code-LLM more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, Awesome-Code-LLM: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/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-huybery-awesome-code-llm.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 Awesome-Code-LLM? bigcode-evaluation-harness: Dormant. Awesome-Code-LLM: 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 Awesome-Code-LLM? 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); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/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/_