Home/Compare/DeepSeek-R1 vs flower

Comparison

DeepSeek-R1 vs flower

Verdict

Pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, flower is Apache-2.0; pick flower when license: flower is Apache-2.0, DeepSeek-R1 is MIT.

Markdown twin · DeepSeek-R1 alternatives · flower alternatives

GraphCanon updated today

DeepSeek-R1 logo

DeepSeek-R1

deepseek-ai/DeepSeek-R1

92kpushed Jun 27, 2025
vs
flower logo

flower

flwrlabs/flower

7.0kpushed Jul 10, 2026

Trust & integrity

SignalDeepSeek-R1flower
Maintenance
Dormant (379d since push)
As of today · github_public_v1
Very active (1d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none

Tagline

DeepSeek-R1
Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.
flower
Flower: A Friendly Federated AI Framework

Stars

DeepSeek-R1
92k
flower
7.0k

Forks

DeepSeek-R1
12k
flower
1.2k

Open issues

DeepSeek-R1
45
flower
329

Language

DeepSeek-R1
-
flower
Python

Adopt for

DeepSeek-R1
DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
flower
-

Persona

DeepSeek-R1
-
flower
-

Runtime

DeepSeek-R1
-
flower
-

License

DeepSeek-R1
MIT
flower
Apache-2.0

Last pushed

DeepSeek-R1
Jun 27, 2025
flower
Jul 10, 2026

Categories

DeepSeek-R1
Model Training, LLM Frameworks
flower
LLM Frameworks, Model Training, Computer Vision

Trust and health

Maintenance

DeepSeek-R1
Dormant (18%)
flower
Very active (96%)

Days since push

DeepSeek-R1
379d
flower
1d

Open issues (now)

DeepSeek-R1
45
flower
329

Full report

DeepSeek-R1
Trust report

Choose DeepSeek-R1 if…

  • License: DeepSeek-R1 is MIT, flower 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 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.

Choose flower if…

  • License: flower is Apache-2.0, DeepSeek-R1 is MIT.
  • Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android.
  • Also covers Computer Vision.

When NOT to use flower

  • 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.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: DeepSeek-R1 92k · flower 7.0k (synced Jul 12, 2026).

Common questions

What is the difference between DeepSeek-R1 and flower?
DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. flower: Flower: A Friendly Federated AI Framework. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSeek-R1 over flower?
Choose DeepSeek-R1 over flower when License: DeepSeek-R1 is MIT, flower 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 flower over DeepSeek-R1?
Choose flower over DeepSeek-R1 when License: flower is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to flower: federated-analytics, deep-learning, federated-learning-framework, android; Also covers Computer Vision.
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 flower?
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.
Is DeepSeek-R1 or flower more popular on GitHub?
DeepSeek-R1 has more GitHub stars (91,991 vs 7,027). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSeek-R1 and flower open source?
Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, flower: Apache-2.0).
Where can I find alternatives to DeepSeek-R1 or flower?
GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and flower alternatives (DeepSeek-R1 markdown twin, flower markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, DeepSeek-R1 or flower?
DeepSeek-R1: Dormant. flower: 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 flower?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; flower trust report.