Comparison
Eagle vs EAGLE
Verdict
Pick Eagle if eagle: Frontier Vision-Language Models with Data-Centric Strategies; pick EAGLE if eAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding.
Markdown twin · Eagle alternatives · EAGLE alternatives
GraphCanon updated 1d
Trust & integrity
| Signal | Eagle | EAGLE |
|---|---|---|
| Maintenance | Steady (54d since push) As of 1w · github_public_v1 | Slowing (155d since push) As of 1mo · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 1mo · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Eagle
- Frontier Vision-Language Models with Data-Centric Strategies
- EAGLE
- Official Implementation of EAGLE Series Models
Stars
- Eagle
- 3.4k
- EAGLE
- 2.5k
Forks
- Eagle
- 327
- EAGLE
- 297
Open issues
- Eagle
- 62
- EAGLE
- 101
Language
- Eagle
- Python
- EAGLE
- Python
Adopt for
- Eagle
- Eagle: Frontier Vision-Language Models with Data-Centric Strategies
- EAGLE
- EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding.
Persona
- Eagle
- -
- EAGLE
- -
Runtime
- Eagle
- -
- EAGLE
- -
License
- Eagle
- The code is released under Apache 2.0 license, while the pretrained models are under CC BY-NC 4.0 or NVIDIA licenses for non-commercial use only.
- EAGLE
- Other
Last pushed
- Eagle
- Jun 24, 2026
- EAGLE
- Feb 20, 2026
Categories
- Eagle
- Computer Vision, LLM Frameworks
- EAGLE
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- Eagle
- Steady (60%)
- EAGLE
- Slowing (36%)
Days since push
- Eagle
- 54d
- EAGLE
- 155d
Open issues (now)
- Eagle
- 62
- EAGLE
- 101
Stars delta
- Eagle
- +199 (30d)
- EAGLE
- Unknown
Open issues delta
- Eagle
- +3 (30d)
- EAGLE
- Unknown
Full report
- Eagle
- Trust report
- EAGLE
- Trust report
Choose Eagle if…
- License: Eagle is Apache-2.0, EAGLE is Other.
- Pricing: Free for non-commercial use; requires adherence to licensing agreements.
- Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights..
- Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements.
- Also covers Computer Vision.
- When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen.
When NOT to use Eagle
- If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License.
- In situations where you require a vision-language model that does not rely on improvements made using Qwen.
Choose EAGLE if…
- License: EAGLE is Other, Eagle is Apache-2.0.
- Tags unique to EAGLE: large language models, llm-inference, speculative-decoding.
- Also covers Inference & Serving.
- If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3.
When NOT to use EAGLE
- If the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project.
- In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (NVlabs/Eagle) · observed Aug 18, 2026
- GitHub forks (NVlabs/Eagle) · observed Aug 18, 2026
- Last push (NVlabs/Eagle) · observed Jun 24, 2026
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (SafeAILab/EAGLE) · observed Aug 24, 2026
- GitHub forks (SafeAILab/EAGLE) · observed Aug 24, 2026
- Last push (SafeAILab/EAGLE) · observed Feb 20, 2026
- License file (Other) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Eagle 3.4k · EAGLE 2.5k (synced Aug 18, 2026).
Common questions
- What is the difference between Eagle and EAGLE?
- Eagle: Frontier Vision-Language Models with Data-Centric Strategies. EAGLE: Official Implementation of EAGLE Series Models. See the comparison table for live GitHub stats and shared categories.
- When should I choose Eagle over EAGLE?
- Choose Eagle over EAGLE when License: Eagle is Apache-2.0, EAGLE is Other; Pricing: Free for non-commercial use; requires adherence to licensing agreements; Requirements: Min 8 GB RAM; Ensure compliance with all applicable laws and regulations when using the dataset and model weights.; Tags unique to Eagle: data-centric-strategies, gpt4, huggingface, llm-improvements; Also covers Computer Vision; When you need advanced vision-language models enhanced by data-centric strategies developed by NVlabs and improved using Qwen.
- When should I choose EAGLE over Eagle?
- Choose EAGLE over Eagle when License: EAGLE is Other, Eagle is Apache-2.0; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; Also covers Inference & Serving; If your project requires the latest advancements in model capabilities from ICML'24, EMNLP'24, and NeurIPS'25 as provided by EAGLE-1, EAGLE-2, or EAGLE-3.
- When should I avoid Eagle?
- If your project requires commercial use, as Eagle's models are intended for non-commercial use only under the CC BY-NC 4.0 License or NVIDIA License. In situations where you require a vision-language model that does not rely on improvements made using Qwen.
- When should I avoid EAGLE?
- If the specific advancements and techniques implemented in ICML'24 papers are not relevant to your project. In cases where speculative decoding does not align with the goals or methods of your application, opting for EAGLE may not be beneficial.
- Is Eagle or EAGLE more popular on GitHub?
- Eagle has more GitHub stars (3,407 vs 2,510). Stars measure visibility, not whether either tool fits your constraints.
- Are Eagle and EAGLE open source?
- Yes - both are open-source projects on GitHub (Eagle: Apache-2.0, EAGLE: Other).
- Where can I find alternatives to Eagle or EAGLE?
- GraphCanon lists graph-backed alternatives at Eagle alternatives and EAGLE alternatives (Eagle 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, Eagle or EAGLE?
- Eagle: Steady. EAGLE: Slowing. 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 Eagle and EAGLE?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Eagle trust report; EAGLE trust report.