--- title: "Awesome-Code-LLM vs SWE-bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-swe-bench-swe-bench" tools: ["huybery-awesome-code-llm", "swe-bench-swe-bench"] --- # Awesome-Code-LLM vs SWE-bench *GraphCanon updated Aug 6, 2026* ## Verdict 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; pick SWE-bench if sWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub. [Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 4 open issues, last pushed Dec 10, 2024. [SWE-bench](https://www.swebench.com) has 5.6k stars, 930 forks, and 131 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [SWE-bench's repository](https://github.com/SWE-bench/SWE-bench). | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [SWE-bench](/tools/swe-bench-swe-bench.md) | | --- | --- | --- | | Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | Benchmark for assessing language models' capability to resolve real-world Github issues | | Stars | 1,291 | 5,576 | | Forks | 74 | 930 | | Open issues | 4 | 131 | | Language | - | Python | | 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. | SWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | The tool operates under the MIT license, detailed in LICENSE.md. | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [SWE-bench](/tools/swe-bench-swe-bench.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 604d | 9d | | Open issues (now) | 4 | 131 | | Owner type | User | Organization | | Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/swe-bench-swe-bench/trust.md) | ## 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. ## Decision facts: SWE-bench - **Adopt for:** SWE-bench serves as a benchmark for assessing how well language models can tackle real-world software engineering issues from GitHub. - **License detail:** The tool operates under the MIT license, detailed in LICENSE.md. ## Choose when ### Choose Awesome-Code-LLM if… - 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. ### Choose SWE-bench if… - Tags unique to SWE-bench: benchmark, language-model, software-engineering. - When you need to evaluate the effectiveness of your language model in resolving practical software engineering challenges found in open-source repositories like GitHub. - More GitHub stars (5.6k vs 1.3k) - visibility, not fit. ## 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 ## When NOT to use SWE-bench - Do not use SWE-bench if your language model's primary application is outside the context of real-world GitHub issue resolution. - Avoid using this tool if you are not interested in testing AI systems' capabilities across visual software domains; it's more specialized for that specific area, unlike general-purpose benchmarks. ## Common questions ### What is the difference between Awesome-Code-LLM and SWE-bench? Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. SWE-bench: Benchmark for assessing language models' capability to resolve real-world Github issues. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Code-LLM over SWE-bench? Choose Awesome-Code-LLM over SWE-bench when 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 choose SWE-bench over Awesome-Code-LLM? Choose SWE-bench over Awesome-Code-LLM when Tags unique to SWE-bench: benchmark, language-model, software-engineering; When you need to evaluate the effectiveness of your language model in resolving practical software engineering challenges found in open-source repositories like GitHub; More GitHub stars (5.6k vs 1.3k) - visibility, not fit. ### 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 ### When should I avoid SWE-bench? Do not use SWE-bench if your language model's primary application is outside the context of real-world GitHub issue resolution. Avoid using this tool if you are not interested in testing AI systems' capabilities across visual software domains; it's more specialized for that specific area, unlike general-purpose benchmarks. ### Is Awesome-Code-LLM or SWE-bench more popular on GitHub? SWE-bench has more GitHub stars (5,576 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Code-LLM and SWE-bench open source? Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, SWE-bench: MIT). ### Where can I find alternatives to Awesome-Code-LLM or SWE-bench? GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [SWE-bench alternatives](/tools/swe-bench-swe-bench/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [SWE-bench markdown twin](/tools/swe-bench-swe-bench/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/huybery-awesome-code-llm-vs-swe-bench-swe-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Code-LLM or SWE-bench? Awesome-Code-LLM: Dormant. SWE-bench: Active. 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 Awesome-Code-LLM and SWE-bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [SWE-bench trust report](/tools/swe-bench-swe-bench/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm) - 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/_