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
vs
Trust & integrity
| Signal | DeepSeek-R1 | Eagle |
|---|---|---|
| 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
- Eagle
- 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 (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- GitHub forks (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- Last push (deepseek-ai/DeepSeek-R1) · observed Jun 27, 2025
- License file (MIT) · observed Jul 12, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVlabs/Eagle) · observed Jul 11, 2026
- GitHub forks (NVlabs/Eagle) · observed Jul 11, 2026
- Last push (NVlabs/Eagle) · observed Jun 24, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
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.