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Comparison

code-eval vs Awesome-Code-LLM

Verdict

Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; 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.

Markdown twin · code-eval alternatives · Awesome-Code-LLM alternatives

GraphCanon updated Oct 6, 2026

code-eval logo

code-eval

abacaj/code-eval

★ 432pushed Sep 12, 2023
vs
Awesome-Code-LLM logo

Awesome-Code-LLM

huybery/Awesome-Code-LLM

★ 1.3kpushed Dec 10, 2024

Trust & integrity

Signalcode-evalAwesome-Code-LLM
Maintenance
Dormant (1120d since push)
As of Oct 6, 2026 · github_public_v1
Dormant (635d since push)
As of Sep 6, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Oct 6, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 6, 2026 · github_public_v1
OSV dependency advisories
Published findings
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 11, 2026 · 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
code-eval trust reportAwesome-Code-LLM trust reportTrust methodology

Tagline

code-eval
Run evaluation on LLMs using human-eval benchmark.
Awesome-Code-LLM
👨💻 An awesome and curated list of best code-LLM for research.

Stars

code-eval
432
Awesome-Code-LLM
1.3k

Forks

code-eval
36
Awesome-Code-LLM
75

Open issues

code-eval
5
Awesome-Code-LLM
5

Language

code-eval
Python
Awesome-Code-LLM
-

Adopt for

code-eval
code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability.
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.

Persona

code-eval
-
Awesome-Code-LLM
-

Runtime

code-eval
-
Awesome-Code-LLM
-

License

code-eval
MIT
Awesome-Code-LLM
MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions.

Last pushed

code-eval
Sep 12, 2023
Awesome-Code-LLM
Dec 10, 2024

Categories

code-eval
Evaluation & Observability
Awesome-Code-LLM
Evaluation & Observability, LLM Frameworks

Trust and health

Days since push

code-eval
1120d
Awesome-Code-LLM
635d

Stars delta

code-eval
+1 (30d)
Awesome-Code-LLM
-1 (30d)

Open issues delta

code-eval
0 (30d)
Awesome-Code-LLM
+1 (30d)

OSV dependency advisories

code-eval
Published findings
Awesome-Code-LLM
No lockfile (source not queried)

Full report

code-eval
Trust report
Awesome-Code-LLM
Trust report

Choose code-eval if…

  • Tags unique to code-eval: humaneval, wizardcoder.
  • When you need clear comparisons of pass rates for different LLMs using standardized tests

When NOT to use code-eval

  • If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences
  • For real-time or dynamic evaluations as this repo offers pre-computed static results only
GitHubDetails

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.

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
GitHubDetails

Explore

code-eval trust report →Awesome-Code-LLM trust report →Evaluation & Observability category →LLM Frameworks category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: code-eval 432 · Awesome-Code-LLM 1.3k (synced Oct 6, 2026).

Common questions

What is the difference between code-eval and Awesome-Code-LLM?
code-eval: Run evaluation on LLMs using human-eval benchmark.. 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 code-eval over Awesome-Code-LLM?
Choose code-eval over Awesome-Code-LLM when Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests.
When should I choose Awesome-Code-LLM over code-eval?
Choose Awesome-Code-LLM over code-eval 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 avoid code-eval?
If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only
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 code-eval or Awesome-Code-LLM more popular on GitHub?
Awesome-Code-LLM has more GitHub stars (1,290 vs 432). Stars measure visibility, not whether either tool fits your constraints.
Are code-eval and Awesome-Code-LLM open source?
Yes - both are open-source projects on GitHub (code-eval: MIT, Awesome-Code-LLM: MIT).
Where can I find alternatives to code-eval or Awesome-Code-LLM?
GraphCanon lists graph-backed alternatives at code-eval alternatives and Awesome-Code-LLM alternatives (code-eval markdown twin, Awesome-Code-LLM 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, code-eval or Awesome-Code-LLM?
code-eval: 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 code-eval and Awesome-Code-LLM?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: code-eval trust report; Awesome-Code-LLM trust report.

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