Home/Compare/DeepSeek-R1 vs Eagle

Comparison

DeepSeek-R1 vs Eagle

Verdict

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

Markdown twin · DeepSeek-R1 alternatives · Eagle alternatives

GraphCanon updated today

DeepSeek-R1 logo

DeepSeek-R1

deepseek-ai/DeepSeek-R1

92kpushed Jun 27, 2025
vs
Eagle logo

Eagle

NVlabs/Eagle

3.2kpushed Jun 24, 2026

Trust & integrity

SignalDeepSeek-R1Eagle
Maintenance
Dormant (379d since push)
As of today · github_public_v1
Active (16d 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.
Eagle
Eagle: Frontier Vision-Language Models with Data-Centric Strategies

Stars

DeepSeek-R1
92k
Eagle
3.2k

Forks

DeepSeek-R1
12k
Eagle
301

Open issues

DeepSeek-R1
45
Eagle
57

Language

DeepSeek-R1
-
Eagle
Python

Adopt for

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

Persona

DeepSeek-R1
-
Eagle
-

Runtime

DeepSeek-R1
-
Eagle
-

License

DeepSeek-R1
MIT
Eagle
Apache-2.0

Last pushed

DeepSeek-R1
Jun 27, 2025
Eagle
Jun 24, 2026

Categories

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

Trust and health

Maintenance

DeepSeek-R1
Dormant (18%)
Eagle
Active (82%)

Days since push

DeepSeek-R1
379d
Eagle
16d

Open issues (now)

DeepSeek-R1
45
Eagle
57

Full report

DeepSeek-R1
Trust report

Choose DeepSeek-R1 if…

  • License: DeepSeek-R1 is MIT, Eagle 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 Eagle if…

  • License: Eagle is Apache-2.0, DeepSeek-R1 is MIT.
  • Tags unique to Eagle: llama, gpt4, eagle, large-language-models.
  • Also covers Computer Vision.

When NOT to use Eagle

  • 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 · Eagle 3.2k (synced Jul 12, 2026).

Common questions

What is the difference between DeepSeek-R1 and Eagle?
DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. Eagle: Eagle: Frontier Vision-Language Models with Data-Centric Strategies. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSeek-R1 over Eagle?
Choose DeepSeek-R1 over Eagle when License: DeepSeek-R1 is MIT, Eagle 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 Eagle over DeepSeek-R1?
Choose Eagle over DeepSeek-R1 when License: Eagle is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to Eagle: llama, gpt4, eagle, large-language-models; 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 Eagle?
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 Eagle more popular on GitHub?
DeepSeek-R1 has more GitHub stars (91,991 vs 3,159). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSeek-R1 and Eagle open source?
Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, Eagle: Apache-2.0).
Where can I find alternatives to DeepSeek-R1 or Eagle?
GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and Eagle alternatives (DeepSeek-R1 markdown twin, Eagle 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 Eagle?
DeepSeek-R1: Dormant. Eagle: 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 Eagle?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; Eagle trust report.