--- title: "evalplus vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evalplus-evalplus-vs-huybery-awesome-code-llm" tools: ["evalplus-evalplus", "huybery-awesome-code-llm"] --- # evalplus vs Awesome-Code-LLM *GraphCanon updated Aug 6, 2026* ## Verdict Pick evalplus if evalplus offers tools for rigorously benchmarking and evaluating large language models like GPT-4 and ChatGPT in synthesizing program code using Python under the Apache-2.0 license; 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. [evalplus](https://evalplus.github.io) reports 1.8k GitHub stars, 205 forks, and 71 open issues, last pushed Oct 2, 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 [evalplus's repository](https://github.com/evalplus/evalplus) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [evalplus](/tools/evalplus-evalplus.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | Rigorous evaluation of LLM-synthesized code | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 1,794 | 1,291 | | Forks | 205 | 74 | | Open issues | 71 | 4 | | Language | Python | - | | Adopt for | evalplus offers tools for rigorously benchmarking and evaluating large language models like GPT-4 and ChatGPT in synthesizing program code using Python under the Apache-2.0 license. | 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 | Apache-2.0 | 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._ | | [evalplus](/tools/evalplus-evalplus.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 306d | 604d | | Open issues (now) | 71 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/evalplus-evalplus/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: evalplus - **Adopt for:** evalplus offers tools for rigorously benchmarking and evaluating large language models like GPT-4 and ChatGPT in synthesizing program code using Python 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 evalplus if… - License: evalplus is Apache-2.0, Awesome-Code-LLM is MIT. - Tags unique to evalplus: benchmark, chatgpt, efficiency, program-synthesis. - evalplus ships Docker support for self-hosted deployment. - When you need advanced benchmarks specific to large language model generated program synthesis, such as from GPT-4 or ChatGPT. ### Choose Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, evalplus 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, code generation, 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 evalplus - Avoid if you require broad language model benchmarking that is not specifically focused on program synthesis capabilities. - Do not use evalplus if your project does not benefit from or need Docker-based isolation for code execution safety measures, such as in controlled lab environments without external dependencies. ## 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 evalplus and Awesome-Code-LLM? evalplus: Rigorous evaluation of LLM-synthesized code. 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 evalplus over Awesome-Code-LLM? Choose evalplus over Awesome-Code-LLM when License: evalplus is Apache-2.0, Awesome-Code-LLM is MIT; Tags unique to evalplus: benchmark, chatgpt, efficiency, program-synthesis; evalplus ships Docker support for self-hosted deployment; When you need advanced benchmarks specific to large language model generated program synthesis, such as from GPT-4 or ChatGPT. ### When should I choose Awesome-Code-LLM over evalplus? Choose Awesome-Code-LLM over evalplus when License: Awesome-Code-LLM is MIT, evalplus 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, code generation, 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 evalplus? Avoid if you require broad language model benchmarking that is not specifically focused on program synthesis capabilities. Do not use evalplus if your project does not benefit from or need Docker-based isolation for code execution safety measures, such as in controlled lab environments without external dependencies. ### 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 evalplus or Awesome-Code-LLM more popular on GitHub? evalplus has more GitHub stars (1,794 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are evalplus and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (evalplus: Apache-2.0, Awesome-Code-LLM: MIT). ### Where can I find alternatives to evalplus or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [evalplus alternatives](/tools/evalplus-evalplus/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([evalplus markdown twin](/tools/evalplus-evalplus/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/evalplus-evalplus-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, evalplus or Awesome-Code-LLM? evalplus: Slowing. 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 evalplus and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalplus trust report](/tools/evalplus-evalplus/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evalplus-evalplus`](/api/graphcanon/graph?tool=evalplus-evalplus) - 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/_