--- title: "FullStackBench vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bytedance-fullstackbench-vs-huybery-awesome-code-llm" tools: ["bytedance-fullstackbench", "huybery-awesome-code-llm"] --- # FullStackBench vs Awesome-Code-LLM *GraphCanon updated Aug 6, 2026* ## Verdict Pick FullStackBench if fullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples; 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. [FullStackBench](https://github.com/bytedance/FullStackBench) reports 121 GitHub stars, 10 forks, and 1 open issues, last pushed May 7, 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 [FullStackBench's repository](https://github.com/bytedance/FullStackBench) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [FullStackBench](/tools/bytedance-fullstackbench.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | Multilingual benchmark for evaluating LLMs in full-stack coding | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 121 | 1,291 | | Forks | 10 | 74 | | Open issues | 1 | 4 | | Language | Python | - | | Adopt for | FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples. | 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._ | | [FullStackBench](/tools/bytedance-fullstackbench.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Days since push | 455d | 604d | | Open issues (now) | 1 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/bytedance-fullstackbench/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: FullStackBench - **Adopt for:** FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples. ## 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 FullStackBench if… - License: FullStackBench is Apache-2.0, Awesome-Code-LLM is MIT. - Tags unique to FullStackBench: benchmarks, full stack coding, llm-evaluation. - When you need to assess LLM performance in full-stack programming tasks covering multiple domains and languages ### Choose Awesome-Code-LLM if… - License: Awesome-Code-LLM is MIT, FullStackBench 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 FullStackBench - Avoid if testing scope is limited to a single or few programming languages as FullStackBench covers a wide range of languages - Not suitable if your focus is solely on theoretical coding challenges instead of practical, full-stack tasks ## 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 FullStackBench and Awesome-Code-LLM? FullStackBench: Multilingual benchmark for evaluating LLMs in full-stack coding. 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 FullStackBench over Awesome-Code-LLM? Choose FullStackBench over Awesome-Code-LLM when License: FullStackBench is Apache-2.0, Awesome-Code-LLM is MIT; Tags unique to FullStackBench: benchmarks, full stack coding, llm-evaluation; When you need to assess LLM performance in full-stack programming tasks covering multiple domains and languages. ### When should I choose Awesome-Code-LLM over FullStackBench? Choose Awesome-Code-LLM over FullStackBench when License: Awesome-Code-LLM is MIT, FullStackBench 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 FullStackBench? Avoid if testing scope is limited to a single or few programming languages as FullStackBench covers a wide range of languages Not suitable if your focus is solely on theoretical coding challenges instead of practical, full-stack tasks ### 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 FullStackBench or Awesome-Code-LLM more popular on GitHub? Awesome-Code-LLM has more GitHub stars (1,291 vs 121). Stars measure visibility, not whether either tool fits your constraints. ### Are FullStackBench and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (FullStackBench: Apache-2.0, Awesome-Code-LLM: MIT). ### Where can I find alternatives to FullStackBench or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [FullStackBench alternatives](/tools/bytedance-fullstackbench/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([FullStackBench markdown twin](/tools/bytedance-fullstackbench/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/bytedance-fullstackbench-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, FullStackBench or Awesome-Code-LLM? FullStackBench: 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 FullStackBench and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FullStackBench trust report](/tools/bytedance-fullstackbench/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bytedance-fullstackbench`](/api/graphcanon/graph?tool=bytedance-fullstackbench) - 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/_