--- title: "LLM4Decompile vs Awesome-Code-LLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/albertan017-llm4decompile-vs-huybery-awesome-code-llm" tools: ["albertan017-llm4decompile", "huybery-awesome-code-llm"] --- # LLM4Decompile vs Awesome-Code-LLM *GraphCanon updated Aug 17, 2026* ## Verdict Pick LLM4Decompile if lLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code; 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. [LLM4Decompile](https://aclanthology.org/2024.emnlp-main.203) reports 7.0k GitHub stars, 546 forks, and 46 open issues, last pushed Feb 12, 2026. [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 [LLM4Decompile's repository](https://github.com/albertan017/LLM4Decompile) and [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM). | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Tagline | Decompiling Binary Code with Large Language Models | 👨💻 An awesome and curated list of best code-LLM for research. | | Stars | 6,965 | 1,291 | | Forks | 546 | 74 | | Open issues | 46 | 4 | | Language | Python | - | | Adopt for | LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. | 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 | MIT | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | | Categories | LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 186d | 604d | | Open issues (now) | 46 | 4 | | Stars delta | +205 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/albertan017-llm4decompile/trust.md) | [trust report](/tools/huybery-awesome-code-llm/trust.md) | ## Decision facts: LLM4Decompile - **Pricing:** freemium - The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs. - **Requirements:** Min 16 GB RAM; Requires a GPU for optimal performance with the specified model. - **Adopt for:** LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. ## 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 LLM4Decompile if… - Pricing: The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs.. - Requirements: Min 16 GB RAM; Requires a GPU for optimal performance with the specified model.. - Tags unique to LLM4Decompile: binary, decompile, reverse-engineering. - LLM4Decompile ships Docker support for self-hosted deployment. - When you need a tool that leverages advanced language models for decompiling binaries more effectively than traditional methods. ### 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. - Also covers Evaluation & Observability. - When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ## When NOT to use LLM4Decompile - Avoid this tool if you require high precision in recreating exact source code, especially for heavily optimized binaries that lose contextual information during compilation. - Do not use LLM4Decompile when working with less common architectures (e.g., RISC-V) unless explicitly supported or tested by the model. ## 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 LLM4Decompile and Awesome-Code-LLM? LLM4Decompile: Decompiling Binary Code with Large 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 LLM4Decompile over Awesome-Code-LLM? Choose LLM4Decompile over Awesome-Code-LLM when Pricing: The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs.; Requirements: Min 16 GB RAM; Requires a GPU for optimal performance with the specified model.; Tags unique to LLM4Decompile: binary, decompile, reverse-engineering; LLM4Decompile ships Docker support for self-hosted deployment; When you need a tool that leverages advanced language models for decompiling binaries more effectively than traditional methods. ### When should I choose Awesome-Code-LLM over LLM4Decompile? Choose Awesome-Code-LLM over LLM4Decompile 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; Also covers Evaluation & Observability; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval. ### When should I avoid LLM4Decompile? Avoid this tool if you require high precision in recreating exact source code, especially for heavily optimized binaries that lose contextual information during compilation. Do not use LLM4Decompile when working with less common architectures (e.g., RISC-V) unless explicitly supported or tested by the model. ### 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 LLM4Decompile or Awesome-Code-LLM more popular on GitHub? LLM4Decompile has more GitHub stars (6,965 vs 1,291). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM4Decompile and Awesome-Code-LLM open source? Yes - both are open-source projects on GitHub (LLM4Decompile: MIT, Awesome-Code-LLM: MIT). ### Where can I find alternatives to LLM4Decompile or Awesome-Code-LLM? GraphCanon lists graph-backed alternatives at [LLM4Decompile alternatives](/tools/albertan017-llm4decompile/alternatives) and [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) ([LLM4Decompile markdown twin](/tools/albertan017-llm4decompile/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/albertan017-llm4decompile-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, LLM4Decompile or Awesome-Code-LLM? LLM4Decompile: 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 LLM4Decompile and Awesome-Code-LLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM4Decompile trust report](/tools/albertan017-llm4decompile/trust); [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=albertan017-llm4decompile`](/api/graphcanon/graph?tool=albertan017-llm4decompile) - 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/_