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Home/Compare/MultiPL-E vs LLMSurvey

Comparison

MultiPL-E vs LLMSurvey

Verdict

Pick MultiPL-E if multiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages; pick LLMSurvey if lLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训.

Markdown twin · MultiPL-E alternatives · LLMSurvey alternatives

GraphCanon updated 4d

MultiPL-E logo

MultiPL-E

nuprl/MultiPL-E

★ 313pushed Apr 12, 2026
vs
LLMSurvey logo

LLMSurvey

RUCAIBox/LLMSurvey

★ 12kpushed Mar 11, 2025

Trust & integrity

SignalMultiPL-ELLMSurvey
Maintenance
Slowing (115d since push)
As of 2w · github_public_v1
Dormant (523d since push)
As of 4d · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 4d · 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
MultiPL-E trust reportLLMSurvey trust reportTrust methodology

Tagline

MultiPL-E
A multi-programming language benchmark for LLMs
LLMSurvey
A comprehensive collection of papers and resources related to Large Language Models.

Stars

MultiPL-E
313
LLMSurvey
12k

Forks

MultiPL-E
57
LLMSurvey
931

Open issues

MultiPL-E
16
LLMSurvey
30

Language

MultiPL-E
Python
LLMSurvey
Python

Adopt for

MultiPL-E
MultiPL-E is a benchmark system translating Python-based coding challenges across multiple programming languages.
LLMSurvey
LLMSurvey is a comprehensive resource center dedicated to large language model research, collecting and organizing scholarly materials and resources relevant to chain-of-thought reasoning, in-context learning, RLHF, and训

Persona

MultiPL-E
-
LLMSurvey
-

Runtime

MultiPL-E
-
LLMSurvey
-

License

MultiPL-E
Other
LLMSurvey
The license for LLMSurvey is unknown based on the provided repository information.

Last pushed

MultiPL-E
Apr 12, 2026
LLMSurvey
Mar 11, 2025

Categories

MultiPL-E
Evaluation & Observability, LLM Frameworks
LLMSurvey
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

MultiPL-E
Slowing (36%)
LLMSurvey
Dormant (18%)

Days since push

MultiPL-E
115d
LLMSurvey
523d

Open issues (now)

MultiPL-E
16
LLMSurvey
30

Stars delta

MultiPL-E
Unknown
LLMSurvey
+18 (30d)

Open issues delta

MultiPL-E
Unknown
LLMSurvey
0 (30d)

Full report

MultiPL-E
Trust report
LLMSurvey
Trust report

Choose MultiPL-E if…

  • Pricing: Free to use but requires local compute resources and potentially licensed libraries.
  • Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark.
  • Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch.

When NOT to use MultiPL-E

  • Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead.
  • Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code.
GitHubDetails

Choose LLMSurvey if…

  • Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage.
  • Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models.
  • You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.

When NOT to use LLMSurvey

  • You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers.
  • Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how
GitHubDetails

Explore

MultiPL-E trust report →LLMSurvey 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: MultiPL-E 313 · LLMSurvey 12k (synced Aug 5, 2026).

Common questions

What is the difference between MultiPL-E and LLMSurvey?
MultiPL-E: A multi-programming language benchmark for LLMs. LLMSurvey: A comprehensive collection of papers and resources related to Large Language Models.. See the comparison table for live GitHub stats and shared categories.
When should I choose MultiPL-E over LLMSurvey?
Choose MultiPL-E over LLMSurvey when Pricing: Free to use but requires local compute resources and potentially licensed libraries; Tags unique to MultiPL-E: ai benchmark, benchmarking, code generation, multilingual benchmark; Use MultiPL-E for evaluating large language models' performance on code generation tasks in different languages directly without needing to create new benchmarks from scratch.
When should I choose LLMSurvey over MultiPL-E?
Choose LLMSurvey over MultiPL-E when Pricing: Since no detailed pricing plan was specified in the repository contents, it can be inferred that access to the materials and resources of LLMSurvey might be free; however, specific details about usage; Tags unique to LLMSurvey: chain-of-thought, in-context-learning, instruction-tuning, large language models; You should use LLMSurvey if you are seeking deep insights into specific advancements such as long chain-of-thought (CoT) reasoning approaches used by DeepSeek-R1 or OpenAI's o-series models.
When should I avoid MultiPL-E?
Avoid using MultiPL-E if you need a more challenging benchmark; consider Ag-LiveCodeBench-X instead. Do not use MultiPL-E if your evaluation environment lacks GPU resources for completion generation or does not support Docker or Podman for execution of generated code.
When should I avoid LLMSurvey?
You might not want to use LLMSurvey if you prefer tools that offer practical implementation details over a survey-style summary and organization of research papers. Consider other resources if your focus is on hands-on development rather than deep academic exploration, as LLMSurvey provides extensive academic coverage but fewer direct coding or implementation how
Is MultiPL-E or LLMSurvey more popular on GitHub?
LLMSurvey has more GitHub stars (12,205 vs 313). Stars measure visibility, not whether either tool fits your constraints.
Are MultiPL-E and LLMSurvey open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to MultiPL-E or LLMSurvey?
GraphCanon lists graph-backed alternatives at MultiPL-E alternatives and LLMSurvey alternatives (MultiPL-E markdown twin, LLMSurvey 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, MultiPL-E or LLMSurvey?
MultiPL-E: Slowing. LLMSurvey: 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 MultiPL-E and LLMSurvey?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: MultiPL-E trust report; LLMSurvey trust report.

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