--- title: "OpenCoder-llm vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/opencoder-llm-opencoder-llm-vs-xlang-ai-ds-1000" tools: ["opencoder-llm-opencoder-llm", "xlang-ai-ds-1000"] --- # OpenCoder-llm vs DS-1000 *GraphCanon updated Aug 5, 2026* ## 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. [OpenCoder-llm](https://opencoder-llm.github.io/) reports 2.1k GitHub stars, 125 forks, and 11 open issues, last pushed Dec 8, 2024. [DS-1000](https://ds1000-code-gen.github.io) has 276 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [OpenCoder-llm's repository](https://github.com/OpenCoder-llm/OpenCoder-llm) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Tagline | The Open Cookbook for Top-Tier Code Large Language Models | Benchmark and code for evaluating large language models on data science tasks | | Stars | 2,103 | 276 | | Forks | 125 | 31 | | Open issues | 11 | 2 | | Language | Python | Python | | Adopt for | OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. | 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 | - | - | | Runtime | - | - | | License | MIT | CC-BY-SA-4.0 | | Categories | Data & Retrieval, Evaluation & Observability, LLM Frameworks, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OpenCoder-llm](/tools/opencoder-llm-opencoder-llm.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Days since push | 604d | 644d | | Open issues (now) | 11 | 2 | | Owner type | User | Organization | | Full report | [trust report](/tools/opencoder-llm-opencoder-llm/trust.md) | [trust report](/tools/xlang-ai-ds-1000/trust.md) | ## Decision facts: OpenCoder-llm - **Adopt for:** OpenCoder-llm offers comprehensive resources for generating high-quality code through its large language models, including datasets, evaluation frameworks, and data pipelines. ## Decision facts: DS-1000 - **Adopt for:** 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. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/opencoder-llm-opencoder-llm/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([OpenCoder-llm markdown twin](/tools/opencoder-llm-opencoder-llm/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/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/opencoder-llm-opencoder-llm-vs-xlang-ai-ds-1000.md) 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](/tools/opencoder-llm-opencoder-llm/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=opencoder-llm-opencoder-llm`](/api/graphcanon/graph?tool=opencoder-llm-opencoder-llm) - 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/_