--- title: "cceval vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amazon-science-cceval-vs-huybery-awesome-code-llm" tools: ["amazon-science-cceval", "huybery-awesome-code-llm"] --- # cceval vs Awesome-Code-LLM *GraphCanon updated Aug 6, 2026* ## Verdict Pick cceval if cceval is designed for assessing cross-file code completion capabilities in multilingual environments across different setups and retrieval methods; 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. [cceval](https://crosscodeeval.github.io/) reports 182 GitHub stars, 28 forks, and 5 open issues, last pushed Aug 15, 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 [cceval's repository](https://github.com/amazon-science/cceval) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [cceval](/tools/amazon-science-cceval.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | CrossCodeEval Benchmark for Cross-File Code Completion | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 182 | 1,291 | | Forks | 28 | 74 | | Open issues | 5 | 4 | | Language | Python | - | | Adopt for | cceval is designed for assessing cross-file code completion capabilities in multilingual environments across different setups and retrieval methods. | 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._ | | [cceval](/tools/amazon-science-cceval.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 354d | 604d | | Open issues (now) | 5 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/amazon-science-cceval/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: cceval - **Adopt for:** cceval is designed for assessing cross-file code completion capabilities in multilingual environments across different setups and retrieval methods. ## 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 cceval if… - License: cceval is Apache-2.0, Awesome-Code-LLM is MIT. - Tags unique to cceval: benchmark, code-completion, cross-file, evaluation-tool. - You need to evaluate the performance of cross-file code completion systems that can handle multiple programming languages simultaneously ### Choose Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, cceval 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 cceval - Your evaluation needs are focused solely on single-file or intralingual code completion benchmarks - You require real-time data access or live updates; cceval provides pre-packaged datasets that need to be manually obtained and uncompressed ## 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 cceval and Awesome-Code-LLM? cceval: CrossCodeEval Benchmark for Cross-File Code Completion. 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 cceval over Awesome-Code-LLM? Choose cceval over Awesome-Code-LLM when License: cceval is Apache-2.0, Awesome-Code-LLM is MIT; Tags unique to cceval: benchmark, code-completion, cross-file, evaluation-tool; You need to evaluate the performance of cross-file code completion systems that can handle multiple programming languages simultaneously. ### When should I choose Awesome-Code-LLM over cceval? Choose Awesome-Code-LLM over cceval when License: Awesome-Code-LLM is MIT, cceval 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 cceval? Your evaluation needs are focused solely on single-file or intralingual code completion benchmarks You require real-time data access or live updates; cceval provides pre-packaged datasets that need to be manually obtained and uncompressed ### 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 cceval or Awesome-Code-LLM more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 182). Stars measure visibility, not whether either tool fits your constraints. ### Are cceval and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (cceval: Apache-2.0, Awesome-Code-LLM: MIT). ### Where can I find alternatives to cceval or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [cceval alternatives](/tools/amazon-science-cceval/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([cceval markdown twin](/tools/amazon-science-cceval/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/amazon-science-cceval-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, cceval or Awesome-Code-LLM? cceval: 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 cceval and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cceval trust report](/tools/amazon-science-cceval/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amazon-science-cceval`](/api/graphcanon/graph?tool=amazon-science-cceval) - 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/_