--- title: "LLM4Decompile vs CodeT5" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/albertan017-llm4decompile-vs-salesforce-codet5" tools: ["albertan017-llm4decompile", "salesforce-codet5"] --- # LLM4Decompile vs CodeT5 *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 CodeT5 if codeT5, an open-source LLM for code understanding and generation under BSD-3-Clause license. [LLM4Decompile](https://aclanthology.org/2024.emnlp-main.203) reports 7.0k GitHub stars, 546 forks, and 46 open issues, last pushed Feb 12, 2026. [CodeT5](https://arxiv.org/abs/2305.07922) has 3.1k stars, 487 forks, and 88 open issues, last pushed Jun 25, 2026. Figures are from public GitHub metadata via [LLM4Decompile's repository](https://github.com/albertan017/LLM4Decompile) and [CodeT5's repository](https://github.com/salesforce/CodeT5). | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [CodeT5](/tools/salesforce-codet5.md) | | --- | --- | --- | | Tagline | Decompiling Binary Code with Large Language Models | Home of CodeT5: Open Code LLMs for Code Understanding and Generation | | Stars | 6,965 | 3,098 | | Forks | 546 | 487 | | Open issues | 46 | 88 | | Language | Python | Python | | Adopt for | LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. | CodeT5, an open-source LLM for code understanding and generation under BSD-3-Clause license. | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-3-Clause | | Categories | LLM Frameworks | Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [CodeT5](/tools/salesforce-codet5.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 186d | 38d | | Archived on GitHub | No | Yes | | Open issues (now) | 46 | 88 | | Stars delta | +205 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/albertan017-llm4decompile/trust.md) | [trust report](/tools/salesforce-codet5/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: CodeT5 - **Adopt for:** CodeT5, an open-source LLM for code understanding and generation under BSD-3-Clause license. ## Choose when ### Choose LLM4Decompile if… - License: LLM4Decompile is MIT, CodeT5 is BSD-3-Clause. - 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 CodeT5 if… - License: CodeT5 is BSD-3-Clause, LLM4Decompile is MIT. - Tags unique to CodeT5: code generation, code-intelligence, code-understanding, language-model. - Also covers Developer Tools. - When focusing on Python projects needing advanced code understanding or generation. ## 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 CodeT5 - If the project requires proprietary licensing protection, given its open-source nature under BSD-3-Clause. - Avoid if real-time performance is a crucial requirement since LLMs typically lag in speed. ## Common questions ### What is the difference between LLM4Decompile and CodeT5? LLM4Decompile: Decompiling Binary Code with Large Language Models. CodeT5: Home of CodeT5: Open Code LLMs for Code Understanding and Generation. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM4Decompile over CodeT5? Choose LLM4Decompile over CodeT5 when License: LLM4Decompile is MIT, CodeT5 is BSD-3-Clause; 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 CodeT5 over LLM4Decompile? Choose CodeT5 over LLM4Decompile when License: CodeT5 is BSD-3-Clause, LLM4Decompile is MIT; Tags unique to CodeT5: code generation, code-intelligence, code-understanding, language-model; Also covers Developer Tools; When focusing on Python projects needing advanced code understanding or generation. ### 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 CodeT5? If the project requires proprietary licensing protection, given its open-source nature under BSD-3-Clause. Avoid if real-time performance is a crucial requirement since LLMs typically lag in speed. ### Is LLM4Decompile or CodeT5 more popular on GitHub? LLM4Decompile has more GitHub stars (6,965 vs 3,098). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM4Decompile and CodeT5 open source? Yes - both are open-source projects on GitHub (LLM4Decompile: MIT, CodeT5: BSD-3-Clause). ### Where can I find alternatives to LLM4Decompile or CodeT5? GraphCanon lists graph-backed alternatives at [LLM4Decompile alternatives](/tools/albertan017-llm4decompile/alternatives) and [CodeT5 alternatives](/tools/salesforce-codet5/alternatives) ([LLM4Decompile markdown twin](/tools/albertan017-llm4decompile/alternatives.md), [CodeT5 markdown twin](/tools/salesforce-codet5/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-salesforce-codet5.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM4Decompile or CodeT5? LLM4Decompile: Slowing. CodeT5: Archived. 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 CodeT5? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM4Decompile trust report](/tools/albertan017-llm4decompile/trust); [CodeT5 trust report](/tools/salesforce-codet5/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/_