---
title: "DeepSeek-R1 vs baseline-defenses"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-neelsjain-baseline-defenses"
tools: ["deepseek-ai-deepseek-r1", "neelsjain-baseline-defenses"]
---

# DeepSeek-R1 vs baseline-defenses

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick DeepSeek-R1 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.; pick baseline-defenses when tags unique to baseline-defenses: python.

[DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [baseline-defenses](https://github.com/neelsjain/baseline-defenses) has 34 stars, 1 forks, and 0 open issues, last pushed Oct 26, 2023. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [baseline-defenses's repository](https://github.com/neelsjain/baseline-defenses).

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) |
| --- | --- | --- |
| Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Official Code for "Baseline Defenses for Adversarial Attacks Against Aligned Language Models" |
| Stars | 91,991 | 34 |
| Forks | 11,711 | 1 |
| Open issues | 45 | 0 |
| Language | - | Python |
| Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | - |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | - |
| Categories | LLM Frameworks, Model Training | Model Training, LLM Frameworks, Computer Vision |

## Trust and health

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

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [baseline-defenses](/tools/neelsjain-baseline-defenses.md) |
| --- | --- | --- |
| Days since push | 379d | 989d |
| Open issues (now) | 45 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/neelsjain-baseline-defenses/trust.md) |

## 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 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: derived models, mit license, distilled models, commercial use.
- 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.

### Choose baseline-defenses if…

- Tags unique to baseline-defenses: python.
- Also covers Computer Vision.
- Leaner open-issue backlog (0).

## 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.

## When NOT to use baseline-defenses

- Last GitHub push was 989 days ago (dormant maintenance, Oct 26, 2023). Validate activity before betting a new project on baseline-defenses.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

## Common questions

### What is the difference between DeepSeek-R1 and baseline-defenses?

DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. baseline-defenses: Official Code for "Baseline Defenses for Adversarial Attacks Against Aligned Language Models". See the comparison table for live GitHub stats and shared categories.

### When should I choose DeepSeek-R1 over baseline-defenses?

Choose DeepSeek-R1 over baseline-defenses 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: derived models, mit license, distilled models, commercial use; 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 choose baseline-defenses over DeepSeek-R1?

Choose baseline-defenses over DeepSeek-R1 when Tags unique to baseline-defenses: python; Also covers Computer Vision; Leaner open-issue backlog (0).

### 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.

### When should I avoid baseline-defenses?

Last GitHub push was 989 days ago (dormant maintenance, Oct 26, 2023). Validate activity before betting a new project on baseline-defenses. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

### Is DeepSeek-R1 or baseline-defenses more popular on GitHub?

DeepSeek-R1 has more GitHub stars (91,991 vs 34). Stars measure visibility, not whether either tool fits your constraints.

### Are DeepSeek-R1 and baseline-defenses open source?

Yes - both are open-source projects on GitHub.

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

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

### Which is better maintained, DeepSeek-R1 or baseline-defenses?

DeepSeek-R1: Dormant. baseline-defenses: 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 DeepSeek-R1 and baseline-defenses?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1)
- 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/_