---
title: "DeepSeek-R1 vs P-tuning-v2"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-thudm-p-tuning-v2"
tools: ["deepseek-ai-deepseek-r1", "thudm-p-tuning-v2"]
---

# DeepSeek-R1 vs P-tuning-v2

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, P-tuning-v2 is Apache-2.0; pick P-tuning-v2 when license: P-tuning-v2 is Apache-2.0, DeepSeek-R1 is MIT.

[DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [P-tuning-v2](https://github.com/THUDM/P-tuning-v2) has 2.1k stars, 212 forks, and 35 open issues, last pushed Nov 16, 2023. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [P-tuning-v2's repository](https://github.com/THUDM/P-tuning-v2).

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) |
| --- | --- | --- |
| Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks |
| Stars | 91,991 | 2,075 |
| Forks | 11,711 | 212 |
| Open issues | 45 | 35 |
| 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 | Apache-2.0 |
| Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training, Vector Databases |

## Trust and health

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

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [P-tuning-v2](/tools/thudm-p-tuning-v2.md) |
| --- | --- | --- |
| Days since push | 379d | 968d |
| Open issues (now) | 45 | 35 |
| Security scan | No lockfile | 50 low (50 low) |
| Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/thudm-p-tuning-v2/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…

- License: DeepSeek-R1 is MIT, P-tuning-v2 is Apache-2.0.
- 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 P-tuning-v2 if…

- License: P-tuning-v2 is Apache-2.0, DeepSeek-R1 is MIT.
- Tags unique to P-tuning-v2: p-tuning, python, prompt-tuning, parameter-efficient-learning.
- Also covers Vector Databases.

## 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 P-tuning-v2

- Last GitHub push was 969 days ago (dormant maintenance, Nov 16, 2023). Validate activity before betting a new project on P-tuning-v2.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

## Common questions

### What is the difference between DeepSeek-R1 and P-tuning-v2?

DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. P-tuning-v2: An optimized deep prompt tuning strategy comparable to fine-tuning across scales and tasks. See the comparison table for live GitHub stats and shared categories.

### When should I choose DeepSeek-R1 over P-tuning-v2?

Choose DeepSeek-R1 over P-tuning-v2 when License: DeepSeek-R1 is MIT, P-tuning-v2 is Apache-2.0; 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 P-tuning-v2 over DeepSeek-R1?

Choose P-tuning-v2 over DeepSeek-R1 when License: P-tuning-v2 is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to P-tuning-v2: p-tuning, python, prompt-tuning, parameter-efficient-learning; Also covers Vector Databases.

### 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 P-tuning-v2?

Last GitHub push was 969 days ago (dormant maintenance, Nov 16, 2023). Validate activity before betting a new project on P-tuning-v2. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.

### Is DeepSeek-R1 or P-tuning-v2 more popular on GitHub?

DeepSeek-R1 has more GitHub stars (91,991 vs 2,075). Stars measure visibility, not whether either tool fits your constraints.

### Are DeepSeek-R1 and P-tuning-v2 open source?

Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, P-tuning-v2: Apache-2.0).

### Where can I find alternatives to DeepSeek-R1 or P-tuning-v2?

GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [P-tuning-v2 alternatives](/tools/thudm-p-tuning-v2/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [P-tuning-v2 markdown twin](/tools/thudm-p-tuning-v2/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-thudm-p-tuning-v2.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, DeepSeek-R1 or P-tuning-v2?

DeepSeek-R1: Dormant. P-tuning-v2: 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 P-tuning-v2?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [P-tuning-v2 trust report](/tools/thudm-p-tuning-v2/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/_