---
title: "Reading_groups vs DeepSeek-R1"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/crazyofapple-reading-groups-vs-deepseek-ai-deepseek-r1"
tools: ["crazyofapple-reading-groups", "deepseek-ai-deepseek-r1"]
---

# Reading_groups vs DeepSeek-R1

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick Reading_groups if 用于跟踪、整理和学习大规模语言模型相关的文章、课程材料和实验演示，适用于希望了解最新技术进展、优化策略、应用案例以及深度分析的研究者。; pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.

[Reading_groups](https://github.com/crazyofapple/Reading_groups) reports 202 GitHub stars, 7 forks, and 0 open issues, last pushed Aug 8, 2023. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) has 92k stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. Figures are from public GitHub metadata via [Reading_groups's repository](https://github.com/crazyofapple/Reading_groups) and [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1).

| | [Reading_groups](/tools/crazyofapple-reading-groups.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) |
| --- | --- | --- |
| Tagline | 资源整理和追踪大规模预训练语言模型相关文章 | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. |
| Stars | 202 | 91,991 |
| Forks | 7 | 11,711 |
| Open issues | 0 | 45 |
| Language | - | - |
| Adopt for | 用于跟踪、整理和学习大规模语言模型相关的文章、课程材料和实验演示，适用于希望了解最新技术进展、优化策略、应用案例以及深度分析的研究者。 | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| Categories | Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training | LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Reading_groups](/tools/crazyofapple-reading-groups.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) |
| --- | --- | --- |
| Days since push | 1068d | 379d |
| Open issues (now) | 0 | 45 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/crazyofapple-reading-groups/trust.md) | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) |

## Decision facts: Reading_groups

- **Adopt for:** 用于跟踪、整理和学习大规模语言模型相关的文章、课程材料和实验演示，适用于希望了解最新技术进展、优化策略、应用案例以及深度分析的研究者。

## Decision facts: DeepSeek-R1

- **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.
- **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.
- **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.

## Choose when

### Choose Reading_groups if…

- Tags unique to Reading_groups: chatgpt, gpt-3, gpt-4, large-language-models.
- Also covers Developer Tools, Evaluation & Observability.
- 您想深入理解特定的大规模预训练语言模型（如GPT-4）、其性能测试及其局限性时

### Choose DeepSeek-R1 if…

- Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository..
- Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs..
- Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license.
- When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics.

## When NOT to use Reading_groups

- 如果您主要关注软件层面的开发工具或特定框架的具体实现代码时。Reading_groups专注于论文资源和理论方向，而不是具体实现技巧
- 当您寻求快速入门基础NLP概念或者新手向教程支持时，该库更倾向于高级研究与前沿议题
- 需要深入对比分析S4等颠覆型架构细节，因为此仓库主要关注现有大规模预训练语言模型的演进步骤和技术迭代

## When NOT to use DeepSeek-R1

- Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments.
- If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series.

## Common questions

### What is the difference between Reading_groups and DeepSeek-R1?

Reading_groups: 资源整理和追踪大规模预训练语言模型相关文章. DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. See the comparison table for live GitHub stats and shared categories.

### When should I choose Reading_groups over DeepSeek-R1?

Choose Reading_groups over DeepSeek-R1 when Tags unique to Reading_groups: chatgpt, gpt-3, gpt-4, large-language-models; Also covers Developer Tools, Evaluation & Observability; 您想深入理解特定的大规模预训练语言模型（如GPT-4）、其性能测试及其局限性时.

### When should I choose DeepSeek-R1 over Reading_groups?

Choose DeepSeek-R1 over Reading_groups when Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit license; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics.

### When should I avoid Reading_groups?

如果您主要关注软件层面的开发工具或特定框架的具体实现代码时。Reading_groups专注于论文资源和理论方向，而不是具体实现技巧 当您寻求快速入门基础NLP概念或者新手向教程支持时，该库更倾向于高级研究与前沿议题 需要深入对比分析S4等颠覆型架构细节，因为此仓库主要关注现有大规模预训练语言模型的演进步骤和技术迭代

### When should I avoid DeepSeek-R1?

Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series.

### Is Reading_groups or DeepSeek-R1 more popular on GitHub?

DeepSeek-R1 has more GitHub stars (91,991 vs 202). Stars measure visibility, not whether either tool fits your constraints.

### Are Reading_groups and DeepSeek-R1 open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Reading_groups or DeepSeek-R1?

GraphCanon lists graph-backed alternatives at [Reading_groups alternatives](/tools/crazyofapple-reading-groups/alternatives) and [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) ([Reading_groups markdown twin](/tools/crazyofapple-reading-groups/alternatives.md), [DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/crazyofapple-reading-groups-vs-deepseek-ai-deepseek-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Reading_groups or DeepSeek-R1?

Reading_groups: Dormant. DeepSeek-R1: 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 Reading_groups and DeepSeek-R1?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Reading_groups trust report](/tools/crazyofapple-reading-groups/trust); [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=crazyofapple-reading-groups`](/api/graphcanon/graph?tool=crazyofapple-reading-groups)
- 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/_