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

# DeepSeek-R1 vs verl

*GraphCanon updated Jul 12, 2026*

## Verdict

Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick verl if verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains.

[DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. [verl](https://verl.readthedocs.io/en/latest/index.html) has 22k stars, 4.2k forks, and 1.6k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [verl's repository](https://github.com/verl-project/verl).

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [verl](/tools/verl-project-verl.md) |
| --- | --- | --- |
| Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | A Flexible and Efficient RL Post-Training Framework |
| Stars | 91,991 | 22,425 |
| Forks | 11,711 | 4,201 |
| Open issues | 45 | 1,576 |
| Language | - | Python |
| Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | LLM Frameworks, Model Training | Model Training |

## Trust and health

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

| | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [verl](/tools/verl-project-verl.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 379d | 0d |
| Open issues (now) | 45 | 1.6k |
| Security scan | No lockfile | 2 low (2 low) |
| Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/verl-project-verl/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.

## Decision facts: verl

- **Pricing:** freemium - verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a
- **Requirements:** Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).
- **Adopt for:** verl/HybridFlow is a specialized Python framework for post-training reinforcement learning (RL) that provides detailed documentation and reproducible baselines. It supports PPO and GRPO algorithms and includes Ray Trains

## Choose when

### Choose DeepSeek-R1 if…

- License: DeepSeek-R1 is MIT, verl 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: commercial use, derived models, distilled models, mit license.
- Also covers LLM Frameworks.
- 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 verl if…

- License: verl is Apache-2.0, DeepSeek-R1 is MIT.
- Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a.
- Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM)..
- Tags unique to verl: grpo, post-training, ppo, python.
- Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities.

## 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 verl

- Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity.
- Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements.

## Common questions

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

DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. verl: A Flexible and Efficient RL Post-Training Framework. See the comparison table for live GitHub stats and shared categories.

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

Choose DeepSeek-R1 over verl when License: DeepSeek-R1 is MIT, verl 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: commercial use, derived models, distilled models, mit license; Also covers LLM Frameworks; 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 verl over DeepSeek-R1?

Choose verl over DeepSeek-R1 when License: verl is Apache-2.0, DeepSeek-R1 is MIT; Pricing: verl operates under the Apache-2.0 license and is free and open-source. However, you might incur costs associated with cloud services like AWS SageMaker if you plan to deploy large-scale projects on a; Requirements: Min 8 GB RAM; Ensure your development environment supports Python and the backend systems you intend to use (FSDP or Megatron-LM).; Tags unique to verl: grpo, post-training, ppo, python; Opt for verl if your project requires flexibility in integrating advanced backend systems like FSDP or Megatron-LM to extend RL model capabilities.

### 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 verl?

Avoid verl if your project does not require advanced backend integration with systems like FSDP or Megatron-LM; it might be overkill and introduce unnecessary complexity. Do not use if detailed documentation is less important to your workflow. While verl excels in this area, simpler frameworks may suffice for lighter requirements.

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

DeepSeek-R1 has more GitHub stars (91,991 vs 22,425). Stars measure visibility, not whether either tool fits your constraints.

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

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

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

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

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

DeepSeek-R1: Dormant. verl: Very active. 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 verl?

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