---
title: "chinese-llm-benchmark vs Awesome-LLM-Eval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/jeinlee1991-chinese-llm-benchmark-vs-onejune2018-awesome-llm-eval"
tools: ["jeinlee1991-chinese-llm-benchmark", "onejune2018-awesome-llm-eval"]
---

# chinese-llm-benchmark vs Awesome-LLM-Eval

*GraphCanon updated Aug 7, 2026*

## Verdict

Pick chinese-llm-benchmark if chinese-llm-benchmark (ReLE评测) 是一个专门用于评估中文大规模语言模型的工具，它可以全面评测涵盖商用和开源的大规模语言模型，并提供详细排行榜及超过200万条缺陷数据。它的主要特点是多维度评估能力和丰富的领域覆盖范围。; pick Awesome-LLM-Eval if awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks.

[chinese-llm-benchmark](https://nonelinear.com) reports 6.4k GitHub stars, 261 forks, and 17 open issues, last pushed Aug 4, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 654 stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [chinese-llm-benchmark's repository](https://github.com/jeinlee1991/chinese-llm-benchmark) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval).

| | [chinese-llm-benchmark](/tools/jeinlee1991-chinese-llm-benchmark.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) |
| --- | --- | --- |
| Tagline | ReLE评测：中文AI大模型能力评测 | Curated list for evaluation of large language models |
| Stars | 6,353 | 654 |
| Forks | 261 | 82 |
| Open issues | 17 | 44 |
| Language | - | - |
| Adopt for | chinese-llm-benchmark (ReLE评测) 是一个专门用于评估中文大规模语言模型的工具，它可以全面评测涵盖商用和开源的大规模语言模型，并提供详细排行榜及超过200万条缺陷数据。它的主要特点是多维度评估能力和丰富的领域覆盖范围。 | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [chinese-llm-benchmark](/tools/jeinlee1991-chinese-llm-benchmark.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 2d | 246d |
| Open issues (now) | 17 | 44 |
| Full report | [trust report](/tools/jeinlee1991-chinese-llm-benchmark/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) |

## Decision facts: chinese-llm-benchmark

- **Adopt for:** chinese-llm-benchmark (ReLE评测) 是一个专门用于评估中文大规模语言模型的工具，它可以全面评测涵盖商用和开源的大规模语言模型，并提供详细排行榜及超过200万条缺陷数据。它的主要特点是多维度评估能力和丰富的领域覆盖范围。

## Decision facts: Awesome-LLM-Eval

- **Pricing:** freemium - The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.
- **Requirements:** The resources listed may vary in their own requirements, including software dependencies and hardware specifications.
- **Adopt for:** Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks.

## Choose when

### Choose chinese-llm-benchmark if…

- Tags unique to chinese-llm-benchmark: agentic-ai, artificial-intelligence, llm-agent.
- 当需要对多种中文字句生成、理解能力进行综合评价时使用；
- More GitHub stars (6.4k vs 654) - visibility, not fit.

### Choose Awesome-LLM-Eval if…

- Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms..
- Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications..
- Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation.
- When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

## When NOT to use chinese-llm-benchmark

- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

## When NOT to use Awesome-LLM-Eval

- You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform.
- If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

## Common questions

### What is the difference between chinese-llm-benchmark and Awesome-LLM-Eval?

chinese-llm-benchmark: ReLE评测：中文AI大模型能力评测. Awesome-LLM-Eval: Curated list for evaluation of large language models. See the comparison table for live GitHub stats and shared categories.

### When should I choose chinese-llm-benchmark over Awesome-LLM-Eval?

Choose chinese-llm-benchmark over Awesome-LLM-Eval when Tags unique to chinese-llm-benchmark: agentic-ai, artificial-intelligence, llm-agent; 当需要对多种中文字句生成、理解能力进行综合评价时使用；; More GitHub stars (6.4k vs 654) - visibility, not fit.

### When should I choose Awesome-LLM-Eval over chinese-llm-benchmark?

Choose Awesome-LLM-Eval over chinese-llm-benchmark when Pricing: The core resources listed in Awesome-LLM-Eval are freely accessible under MIT license, however, certain datasets or tools might have individual licensing terms.; Requirements: The resources listed may vary in their own requirements, including software dependencies and hardware specifications.; Tags unique to Awesome-LLM-Eval: awesome-list, benchmark, datasets, evaluation; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### When should I avoid chinese-llm-benchmark?

Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

### When should I avoid Awesome-LLM-Eval?

You require real-time testing capabilities or interactive features; Awesome-LLM-Eval is a static resource list and not an interactive platform. If integration with specific third-party platforms or direct API access is necessary, since the repository predominantly serves as a reference point rather than an operational tool.

### Is chinese-llm-benchmark or Awesome-LLM-Eval more popular on GitHub?

chinese-llm-benchmark has more GitHub stars (6,353 vs 654). Stars measure visibility, not whether either tool fits your constraints.

### Are chinese-llm-benchmark and Awesome-LLM-Eval open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to chinese-llm-benchmark or Awesome-LLM-Eval?

GraphCanon lists graph-backed alternatives at [chinese-llm-benchmark alternatives](/tools/jeinlee1991-chinese-llm-benchmark/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([chinese-llm-benchmark markdown twin](/tools/jeinlee1991-chinese-llm-benchmark/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/jeinlee1991-chinese-llm-benchmark-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, chinese-llm-benchmark or Awesome-LLM-Eval?

chinese-llm-benchmark: Very active. Awesome-LLM-Eval: 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 chinese-llm-benchmark and Awesome-LLM-Eval?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [chinese-llm-benchmark trust report](/tools/jeinlee1991-chinese-llm-benchmark/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=jeinlee1991-chinese-llm-benchmark`](/api/graphcanon/graph?tool=jeinlee1991-chinese-llm-benchmark)
- 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/_