---
title: "Awesome-LLM-Eval vs VLMEvalKit"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/onejune2018-awesome-llm-eval-vs-open-compass-vlmevalkit"
tools: ["onejune2018-awesome-llm-eval", "open-compass-vlmevalkit"]
---

# Awesome-LLM-Eval vs VLMEvalKit

*GraphCanon updated Aug 17, 2026*

## Verdict

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; pick VLMEvalKit if vLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models.

[Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) reports 654 GitHub stars, 82 forks, and 44 open issues, last pushed Nov 24, 2025. [VLMEvalKit](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard) has 4.3k stars, 745 forks, and 285 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval) and [VLMEvalKit's repository](https://github.com/open-compass/VLMEvalKit).

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) |
| --- | --- | --- |
| Tagline | Curated list for evaluation of large language models | An open-source evaluation toolkit for large vision-language models |
| Stars | 654 | 4,345 |
| Forks | 82 | 745 |
| Open issues | 44 | 285 |
| Language | - | Python |
| Adopt for | Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks. | VLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | Apache-2.0 |
| Categories | Evaluation & Observability | Evaluation & Observability |

## Trust and health

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

| | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Very active (96%) |
| Days since push | 246d | 0d |
| Open issues (now) | 44 | 285 |
| Stars delta | Unknown | +60 (30d) |
| Open issues delta | Unknown | +21 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | [trust report](/tools/open-compass-vlmevalkit/trust.md) |

## 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.

## Decision facts: VLMEvalKit

- **Adopt for:** VLMEvalKit is an open-source Python evaluation toolkit for large vision-language models that offers one-command evaluation with support for various benchmarks and models.

## Choose when

### Choose Awesome-LLM-Eval if…

- License: Awesome-LLM-Eval is MIT, VLMEvalKit is Apache-2.0.
- 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, leaderboard.
- When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### Choose VLMEvalKit if…

- License: VLMEvalKit is Apache-2.0, Awesome-LLM-Eval is MIT.
- Tags unique to VLMEvalKit: computer-vision, llm, multi-modal.
- When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy.

## 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.

## When NOT to use VLMEvalKit

- If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format.
- When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction.

## Common questions

### What is the difference between Awesome-LLM-Eval and VLMEvalKit?

Awesome-LLM-Eval: Curated list for evaluation of large language models. VLMEvalKit: An open-source evaluation toolkit for large vision-language models. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-LLM-Eval over VLMEvalKit?

Choose Awesome-LLM-Eval over VLMEvalKit when License: Awesome-LLM-Eval is MIT, VLMEvalKit is Apache-2.0; 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, leaderboard; When you specifically need access to an extensive compilation of evaluation-related resources tailored towards large language model assessment.

### When should I choose VLMEvalKit over Awesome-LLM-Eval?

Choose VLMEvalKit over Awesome-LLM-Eval when License: VLMEvalKit is Apache-2.0, Awesome-LLM-Eval is MIT; Tags unique to VLMEvalKit: computer-vision, llm, multi-modal; When you need to evaluate models supporting thinking mode, as it provides a custom split_thinking function improving accuracy.

### 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.

### When should I avoid VLMEvalKit?

If your project requires evaluation tools that generate Excel files with individual cells larger than the default support of 32,767 characters and cannot switch to TSV format. When you do not need generation-based evaluation methods with exact matching and LLM-based answer extraction.

### Is Awesome-LLM-Eval or VLMEvalKit more popular on GitHub?

VLMEvalKit has more GitHub stars (4,345 vs 654). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-LLM-Eval and VLMEvalKit open source?

Yes - both are open-source projects on GitHub (Awesome-LLM-Eval: MIT, VLMEvalKit: Apache-2.0).

### Where can I find alternatives to Awesome-LLM-Eval or VLMEvalKit?

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

### Which is better maintained, Awesome-LLM-Eval or VLMEvalKit?

Awesome-LLM-Eval: Slowing. VLMEvalKit: Very 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 Awesome-LLM-Eval and VLMEvalKit?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval`](/api/graphcanon/graph?tool=onejune2018-awesome-llm-eval)
- 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/_