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Comparison

Awesome-LLM-Eval vs VLMEvalKit

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.

Markdown twin · Awesome-LLM-Eval alternatives · VLMEvalKit alternatives

GraphCanon updated Sep 20, 2026

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Awesome-LLM-Eval logo

Awesome-LLM-Eval

onejune2018/Awesome-LLM-Eval

★ 656pushed Nov 24, 2025
vs
VLMEvalKit logo

VLMEvalKit

open-compass/VLMEvalKit

★ 4.3kpushed Aug 17, 2026

Trust & integrity

SignalAwesome-LLM-EvalVLMEvalKit
Maintenance
Slowing (300d since push)
As of Sep 20, 2026 · github_public_v1
Very active (0d since push)
As of Aug 17, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 20, 2026 · github_public_v1
Not a fork · Organization account
As of Aug 17, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
Published findings
As of Jul 11, 2026 · 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
Awesome-LLM-Eval trust reportVLMEvalKit trust reportTrust methodology

Tagline

Awesome-LLM-Eval
Curated list for evaluation of large language models
VLMEvalKit
An open-source evaluation toolkit for large vision-language models

Stars

Awesome-LLM-Eval
656
VLMEvalKit
4.3k

Forks

Awesome-LLM-Eval
90
VLMEvalKit
745

Open issues

Awesome-LLM-Eval
55
VLMEvalKit
285

Language

Awesome-LLM-Eval
-
VLMEvalKit
Python

Adopt for

Awesome-LLM-Eval
Awesome-LLM-Eval provides a comprehensive curated list of resources for evaluating large language models including tools, datasets, and benchmarks.
VLMEvalKit
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

Awesome-LLM-Eval
-
VLMEvalKit
-

Runtime

Awesome-LLM-Eval
-
VLMEvalKit
-

License

Awesome-LLM-Eval
MIT
VLMEvalKit
Apache-2.0

Last pushed

Awesome-LLM-Eval
Nov 24, 2025
VLMEvalKit
Aug 17, 2026

Categories

Awesome-LLM-Eval
Evaluation & Observability
VLMEvalKit
Evaluation & Observability

Trust and health

Maintenance

Awesome-LLM-Eval
Slowing (36%)
VLMEvalKit
Very active (96%)

Days since push

Awesome-LLM-Eval
300d
VLMEvalKit
0d

Open issues (now)

Awesome-LLM-Eval
55
VLMEvalKit
285

Stars delta

Awesome-LLM-Eval
+2 (30d)
VLMEvalKit
+60 (30d)

Open issues delta

Awesome-LLM-Eval
+11 (30d)
VLMEvalKit
+21 (30d)

Owner type

Awesome-LLM-Eval
User
VLMEvalKit
Organization

OSV dependency advisories

Awesome-LLM-Eval
No lockfile (source not queried)
VLMEvalKit
Published findings

Full report

Awesome-LLM-Eval
Trust report
VLMEvalKit
Trust report

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.

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.
GitHubDetails

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 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.
GitHubDetails

Explore

Awesome-LLM-Eval trust report →VLMEvalKit trust report →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: Awesome-LLM-Eval 656 · VLMEvalKit 4.3k (synced Sep 20, 2026).

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 656). 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 and VLMEvalKit alternatives (Awesome-LLM-Eval markdown twin, VLMEvalKit 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, 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; VLMEvalKit trust report.

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