Comparison
Awesome-Code-LLM vs LiveCodeBench
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 LiveCodeBench if liveCodeBench offers an in-depth approach to evaluating large language models specifically for code tasks such as generation and repair.
Markdown twin · Awesome-Code-LLM alternatives · LiveCodeBench alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | Awesome-Code-LLM | LiveCodeBench |
|---|---|---|
| Maintenance | Dormant (604d since push) As of 2w · github_public_v1 | Dormant (385d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Awesome-Code-LLM
- 👨💻 An awesome and curated list of best code-LLM for research.
- LiveCodeBench
- Holistic and contamination-free evaluation of large language models for code
Stars
- Awesome-Code-LLM
- 1.3k
- LiveCodeBench
- 925
Forks
- Awesome-Code-LLM
- 74
- LiveCodeBench
- 195
Open issues
- Awesome-Code-LLM
- 4
- LiveCodeBench
- 38
Language
- Awesome-Code-LLM
- -
- LiveCodeBench
- Python
Adopt for
- Awesome-Code-LLM
- 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.
- LiveCodeBench
- LiveCodeBench offers an in-depth approach to evaluating large language models specifically for code tasks such as generation and repair.
Persona
- Awesome-Code-LLM
- -
- LiveCodeBench
- -
Runtime
- Awesome-Code-LLM
- -
- LiveCodeBench
- -
License
- Awesome-Code-LLM
- MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions.
- LiveCodeBench
- MIT
Last pushed
- Awesome-Code-LLM
- Dec 10, 2024
- LiveCodeBench
- Jul 16, 2025
Categories
- Awesome-Code-LLM
- Evaluation & Observability, LLM Frameworks
- LiveCodeBench
- Evaluation & Observability
Trust and health
Days since push
- Awesome-Code-LLM
- 604d
- LiveCodeBench
- 385d
Open issues (now)
- Awesome-Code-LLM
- 4
- LiveCodeBench
- 38
Owner type
- Awesome-Code-LLM
- User
- LiveCodeBench
- Organization
Full report
- Awesome-Code-LLM
- Trust report
- LiveCodeBench
- Trust report
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, 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 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
Choose LiveCodeBench if…
- Tags unique to LiveCodeBench: code-execution, code-repair, gpt-4, python.
- When you need a holistic method to assess the effectiveness of LLMs in code tasks without risking contamination by earlier outputs or data leakage.
- More recently updated (last pushed Jul 16, 2025).
When NOT to use LiveCodeBench
- For broad, non-code-specific model assessments where a more generalized evaluation tool would suffice.
- If your project is not compatible with Python 3.11 or if you do not want to use the uv dependency manager recommended by LiveCodeBench.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huybery/Awesome-Code-LLM) · observed Aug 6, 2026
- GitHub forks (huybery/Awesome-Code-LLM) · observed Aug 6, 2026
- Last push (huybery/Awesome-Code-LLM) · observed Dec 10, 2024
- License file (MIT) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (LiveCodeBench/LiveCodeBench) · observed Aug 5, 2026
- GitHub forks (LiveCodeBench/LiveCodeBench) · observed Aug 5, 2026
- Last push (LiveCodeBench/LiveCodeBench) · observed Jul 16, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Code-LLM 1.3k · LiveCodeBench 925 (synced Aug 6, 2026).
Common questions
- What is the difference between Awesome-Code-LLM and LiveCodeBench?
- Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. LiveCodeBench: Holistic and contamination-free evaluation of large language models for code. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Code-LLM over LiveCodeBench?
- Choose Awesome-Code-LLM over LiveCodeBench 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, 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 LiveCodeBench over Awesome-Code-LLM?
- Choose LiveCodeBench over Awesome-Code-LLM when Tags unique to LiveCodeBench: code-execution, code-repair, gpt-4, python; When you need a holistic method to assess the effectiveness of LLMs in code tasks without risking contamination by earlier outputs or data leakage; More recently updated (last pushed Jul 16, 2025).
- 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 LiveCodeBench?
- For broad, non-code-specific model assessments where a more generalized evaluation tool would suffice. If your project is not compatible with Python 3.11 or if you do not want to use the uv dependency manager recommended by LiveCodeBench.
- Is Awesome-Code-LLM or LiveCodeBench more popular on GitHub?
- Awesome-Code-LLM has more GitHub stars (1,291 vs 925). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Code-LLM and LiveCodeBench open source?
- Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, LiveCodeBench: MIT).
- Where can I find alternatives to Awesome-Code-LLM or LiveCodeBench?
- GraphCanon lists graph-backed alternatives at Awesome-Code-LLM alternatives and LiveCodeBench alternatives (Awesome-Code-LLM markdown twin, LiveCodeBench markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Awesome-Code-LLM or LiveCodeBench?
- Awesome-Code-LLM: Dormant. LiveCodeBench: 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 Awesome-Code-LLM and LiveCodeBench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Code-LLM trust report; LiveCodeBench trust report.