--- title: "ACLUE vs EAGLE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/isen-zhang-aclue-vs-safeailab-eagle" tools: ["isen-zhang-aclue", "safeailab-eagle"] --- # ACLUE vs EAGLE *GraphCanon updated Aug 24, 2026* ## Verdict Pick ACLUE if aCLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge; pick EAGLE if eAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. [ACLUE](https://github.com/isen-zhang/ACLUE) reports 34 GitHub stars, 0 forks, and 0 open issues, last pushed Mar 20, 2024. [EAGLE](https://arxiv.org/pdf/2503.01840) has 2.5k stars, 297 forks, and 101 open issues, last pushed Feb 20, 2026. Figures are from public GitHub metadata via [ACLUE's repository](https://github.com/isen-zhang/ACLUE) and [EAGLE's repository](https://github.com/SafeAILab/EAGLE). | | [ACLUE](/tools/isen-zhang-aclue.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Tagline | Evaluation Benchmark for Ancient Chinese Language Comprehension | Official Implementation of EAGLE Series Models | | Stars | 34 | 2,510 | | Forks | 0 | 297 | | Open issues | 0 | 101 | | Language | Python | Python | | Adopt for | ACLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge. | EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. | | Persona | - | - | | Runtime | - | - | | License | MIT License: Permissive open-source license allowing free use and modification of the software, including commercially. | Other | | Categories | Evaluation & Observability | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ACLUE](/tools/isen-zhang-aclue.md) | [EAGLE](/tools/safeailab-eagle.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 868d | 155d | | Open issues (now) | 0 | 101 | | Owner type | User | Organization | | Full report | [trust report](/tools/isen-zhang-aclue/trust.md) | [trust report](/tools/safeailab-eagle/trust.md) | ## Decision facts: ACLUE - **Adopt for:** ACLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge. - **License detail:** MIT License: Permissive open-source license allowing free use and modification of the software, including commercially. ## Decision facts: EAGLE - **Adopt for:** EAGLE offers official implementations for its series of large language models with emphasis on inference and speculative decoding. ## Choose when ### Choose ACLUE if… - License: ACLUE is MIT, EAGLE is Other. - Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks. - Also covers Evaluation & Observability. - When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 tasks ### Choose EAGLE if… - License: EAGLE is Other, ACLUE is MIT. - Tags unique to EAGLE: large language models, llm-inference, speculative-decoding. - Also covers Inference & Serving, LLM Frameworks. - 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 ACLUE - For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension - When the focus is strictly on contemporary texts without a need for historical language understanding capabilities ## 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. ## Common questions ### What is the difference between ACLUE and EAGLE? ACLUE: Evaluation Benchmark for Ancient Chinese Language Comprehension. EAGLE: Official Implementation of EAGLE Series Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose ACLUE over EAGLE? Choose ACLUE over EAGLE when License: ACLUE is MIT, EAGLE is Other; Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks; Also covers Evaluation & Observability; When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 tasks. ### When should I choose EAGLE over ACLUE? Choose EAGLE over ACLUE when License: EAGLE is Other, ACLUE is MIT; Tags unique to EAGLE: large language models, llm-inference, speculative-decoding; Also covers Inference & Serving, LLM Frameworks; 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 ACLUE? For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension When the focus is strictly on contemporary texts without a need for historical language understanding capabilities ### 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 ACLUE or EAGLE more popular on GitHub? EAGLE has more GitHub stars (2,510 vs 34). Stars measure visibility, not whether either tool fits your constraints. ### Are ACLUE and EAGLE open source? Yes - both are open-source projects on GitHub (ACLUE: MIT, EAGLE: Other). ### Where can I find alternatives to ACLUE or EAGLE? GraphCanon lists graph-backed alternatives at [ACLUE alternatives](/tools/isen-zhang-aclue/alternatives) and [EAGLE alternatives](/tools/safeailab-eagle/alternatives) ([ACLUE markdown twin](/tools/isen-zhang-aclue/alternatives.md), [EAGLE markdown twin](/tools/safeailab-eagle/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/isen-zhang-aclue-vs-safeailab-eagle.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ACLUE or EAGLE? ACLUE: Dormant. 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 ACLUE and EAGLE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ACLUE trust report](/tools/isen-zhang-aclue/trust); [EAGLE trust report](/tools/safeailab-eagle/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=isen-zhang-aclue`](/api/graphcanon/graph?tool=isen-zhang-aclue) - 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/_