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Comparison

OpenCoder-llm vs DS-1000

Verdict

Pick OpenCoder-llm if openCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines; pick DS-1000 if the DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.

Markdown twin · OpenCoder-llm alternatives · DS-1000 alternatives

GraphCanon updated 2w

OpenCoder-llm logo

OpenCoder-llm

OpenCoder-llm/OpenCoder-llm

★ 2.1kpushed Dec 8, 2024
vs
DS-1000 logo

DS-1000

xlang-ai/DS-1000

★ 276pushed Oct 30, 2024

Trust & integrity

SignalOpenCoder-llmDS-1000
Maintenance
Dormant (604d since push)
As of 2w · github_public_v1
Dormant (644d 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
OpenCoder-llm trust reportDS-1000 trust reportTrust methodology

Tagline

OpenCoder-llm
The Open Cookbook for Top-Tier Code Large Language Models
DS-1000
Benchmark and code for evaluating large language models on data science tasks

Stars

OpenCoder-llm
2.1k
DS-1000
276

Forks

OpenCoder-llm
125
DS-1000
31

Open issues

OpenCoder-llm
11
DS-1000
2

Language

OpenCoder-llm
Python
DS-1000
Python

Adopt for

OpenCoder-llm
OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines.
DS-1000
The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.

Persona

OpenCoder-llm
-
DS-1000
-

Runtime

OpenCoder-llm
-
DS-1000
-

License

OpenCoder-llm
MIT
DS-1000
CC-BY-SA-4.0

Last pushed

OpenCoder-llm
Dec 8, 2024
DS-1000
Oct 30, 2024

Categories

OpenCoder-llm
Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training
DS-1000
Data & Retrieval, Model Training

Trust and health

Days since push

OpenCoder-llm
604d
DS-1000
644d

Open issues (now)

OpenCoder-llm
11
DS-1000
2

Owner type

OpenCoder-llm
User
DS-1000
Organization

Full report

OpenCoder-llm
Trust report
DS-1000
Trust report

Choose OpenCoder-llm if…

  • License: OpenCoder-llm is MIT, DS-1000 is CC-BY-SA-4.0.
  • Tags unique to OpenCoder-llm: data filtering, dataset, evaluation-framework.
  • Also covers Evaluation & Observability, LLM Frameworks.
  • When you need access to both English and Chinese language support in your code generation tasks.

When NOT to use OpenCoder-llm

  • If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese.
  • For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process.
  • If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary.
  • When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks.
GitHubDetails

Choose DS-1000 if…

  • License: DS-1000 is CC-BY-SA-4.0, OpenCoder-llm is MIT.
  • Tags unique to DS-1000: benchmark, data-science, semantic-parsing.
  • When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.

When NOT to use DS-1000

  • Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python.
  • It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
GitHubDetails

Explore

OpenCoder-llm trust report →DS-1000 trust report →Data & Retrieval category →Evaluation & Observability category →LLM Frameworks category →Model Training 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: OpenCoder-llm 2.1k · DS-1000 276 (synced Aug 5, 2026).

Common questions

What is the difference between OpenCoder-llm and DS-1000?
OpenCoder-llm: The Open Cookbook for Top-Tier Code Large Language Models. DS-1000: Benchmark and code for evaluating large language models on data science tasks. See the comparison table for live GitHub stats and shared categories.
When should I choose OpenCoder-llm over DS-1000?
Choose OpenCoder-llm over DS-1000 when License: OpenCoder-llm is MIT, DS-1000 is CC-BY-SA-4.0; Tags unique to OpenCoder-llm: data filtering, dataset, evaluation-framework; Also covers Evaluation & Observability, LLM Frameworks; When you need access to both English and Chinese language support in your code generation tasks.
When should I choose DS-1000 over OpenCoder-llm?
Choose DS-1000 over OpenCoder-llm when License: DS-1000 is CC-BY-SA-4.0, OpenCoder-llm is MIT; Tags unique to DS-1000: benchmark, data-science, semantic-parsing; When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.
When should I avoid OpenCoder-llm?
If your primary focus is on natural language processing tasks that do not involve code generation or require languages other than English or Chinese. For scenarios where the availability of intermediate checkpoints during pretraining stages does not add value to your development process. If you are working with datasets that already provide synthetic annealing data, and additional resources for this type of data are unnecessary. When a tool without an open-source data cleaning pipeline is sufficient for your code generation tasks.
When should I avoid DS-1000?
Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
Is OpenCoder-llm or DS-1000 more popular on GitHub?
OpenCoder-llm has more GitHub stars (2,103 vs 276). Stars measure visibility, not whether either tool fits your constraints.
Are OpenCoder-llm and DS-1000 open source?
Yes - both are open-source projects on GitHub (OpenCoder-llm: MIT, DS-1000: CC-BY-SA-4.0).
Where can I find alternatives to OpenCoder-llm or DS-1000?
GraphCanon lists graph-backed alternatives at OpenCoder-llm alternatives and DS-1000 alternatives (OpenCoder-llm markdown twin, DS-1000 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, OpenCoder-llm or DS-1000?
OpenCoder-llm: Dormant. DS-1000: 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 OpenCoder-llm and DS-1000?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenCoder-llm trust report; DS-1000 trust report.

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