Home/Compare/athina-evals vs gpt_academic

Comparison

athina-evals vs gpt_academic

Verdict

Pick athina-evals when tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; pick gpt_academic when requirements: Min 8 GB RAM; 依赖Python环境.

Markdown twin · athina-evals alternatives · gpt_academic alternatives

GraphCanon updated 1d

athina-evals logo

athina-evals

athina-ai/athina-evals

301pushed Jun 6, 2025
vs
gpt_academic logo

gpt_academic

binary-husky/gpt_academic

71kpushed Jan 25, 2026

Trust & integrity

Signalathina-evalsgpt_academic
Maintenance
Dormant (399d since push)
As of 1d · github_public_v1
Slowing (166d since push)
As of 1d · github_public_v1
Provenance
Not a fork · Organization account
As of 1d · github_public_v1
Not a fork · Personal account
As of 1d · github_public_v1
Security (OSV)
No lockfile
As of 1d · none
No lockfile
As of 1d · none

Tagline

athina-evals
Python SDK for running evaluations on LLM generated responses
gpt_academic
提供实用化交互接口,优化论文阅读/润色/写作体验

Stars

athina-evals
301
gpt_academic
71k

Forks

athina-evals
22
gpt_academic
8.3k

Open issues

athina-evals
3
gpt_academic
329

Language

athina-evals
Python
gpt_academic
Python

Adopt for

athina-evals
-
gpt_academic
gpt_academic专为增强与GPT/GLM等大语言模型的交互,优化论文写作、润色和阅读体验。它支持自定义模块、多种LLM接入,并且拥有PDF/LaTeX文档处理功能。

Persona

athina-evals
-
gpt_academic
-

Runtime

athina-evals
-
gpt_academic
-

License

athina-evals
-
gpt_academic
使用GPL-3.0许可证,这意味着你可以自由地运行、学习、分享和修改这个软件,但是如果你在分发含gpt_academic的程序时,你必须公开整个程序源代码且采用同为GPL许可证

Last pushed

athina-evals
Jun 6, 2025
gpt_academic
Jan 25, 2026

Categories

athina-evals
Developer Tools, Evaluation & Observability, LLM Frameworks
gpt_academic
Developer Tools, LLM Frameworks

Trust and health

Maintenance

athina-evals
Dormant (18%)
gpt_academic
Slowing (36%)

Days since push

athina-evals
399d
gpt_academic
166d

Open issues (now)

athina-evals
3
gpt_academic
329

Owner type

athina-evals
Organization
gpt_academic
User

Full report

athina-evals
Trust report
gpt_academic
Trust report

Choose athina-evals if…

  • Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval.
  • Also covers Evaluation & Observability.
  • Leaner open-issue backlog (3).

When NOT to use athina-evals

  • Last GitHub push was 400 days ago (dormant maintenance, Jun 6, 2025). Validate activity before betting a new project on athina-evals.
  • Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

Choose gpt_academic if…

  • Requirements: Min 8 GB RAM; 依赖Python环境.
  • Tags unique to gpt_academic: academic, chatglm-6b, chatgpt, gpt-4.
  • gpt_academic ships Docker support for self-hosted deployment.
  • 需要使用GPT或GLM大语言模型进行高效的学术论文相关任务时

When NOT to use gpt_academic

  • 主要任务不是围绕论文和学术资料处理时, 特别是不涉及到大语言模型的实际应用情况
  • 不需要自定义快捷按钮与高级功能插件,且对通用的大语言模型交互界面已经满意
  • 侧重于文本创作以外的开发流程优化(例如:性能调优或低层级编程实现)
  • 只需要基本的数据翻译或总结工具,无需连接多个大语言模型来提升任务效率

Explore

Sources

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

GitHub stars on cards: athina-evals 301 · gpt_academic 71k (synced Jul 11, 2026).

Common questions

What is the difference between athina-evals and gpt_academic?
athina-evals: Python SDK for running evaluations on LLM generated responses. gpt_academic: 提供实用化交互接口,优化论文阅读/润色/写作体验. See the comparison table for live GitHub stats and shared categories.
When should I choose athina-evals over gpt_academic?
Choose athina-evals over gpt_academic when Tags unique to athina-evals: evaluation, evaluation-framework, evaluation-metrics, llm-eval; Also covers Evaluation & Observability; Leaner open-issue backlog (3).
When should I choose gpt_academic over athina-evals?
Choose gpt_academic over athina-evals when Requirements: Min 8 GB RAM; 依赖Python环境; Tags unique to gpt_academic: academic, chatglm-6b, chatgpt, gpt-4; gpt_academic ships Docker support for self-hosted deployment; 需要使用GPT或GLM大语言模型进行高效的学术论文相关任务时.
When should I avoid athina-evals?
Last GitHub push was 400 days ago (dormant maintenance, Jun 6, 2025). Validate activity before betting a new project on athina-evals. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
When should I avoid gpt_academic?
主要任务不是围绕论文和学术资料处理时, 特别是不涉及到大语言模型的实际应用情况 不需要自定义快捷按钮与高级功能插件,且对通用的大语言模型交互界面已经满意 侧重于文本创作以外的开发流程优化(例如:性能调优或低层级编程实现) 只需要基本的数据翻译或总结工具,无需连接多个大语言模型来提升任务效率
Is athina-evals or gpt_academic more popular on GitHub?
gpt_academic has more GitHub stars (71,056 vs 301). Stars measure visibility, not whether either tool fits your constraints.
Are athina-evals and gpt_academic open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to athina-evals or gpt_academic?
GraphCanon lists graph-backed alternatives at athina-evals alternatives and gpt_academic alternatives (athina-evals markdown twin, gpt_academic 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, athina-evals or gpt_academic?
athina-evals: Dormant. gpt_academic: 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 athina-evals and gpt_academic?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; gpt_academic trust report.