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Awesome-Multimodal-Large-Language-Models vs lmms-eval

Awesome-Multimodal-Large-Language-Models (:sparkles::sparkles:Latest Advances on Multimodal Large Language Models) vs lmms-eval (Unified Evaluation Toolkit for Multimodal Large Language Models) - live GitHub stats and typed graph relationships, not marketing.

Markdown twin · Awesome-Multimodal-Large-Language-Models alternatives · lmms-eval alternatives

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Awesome-Multimodal-Large-Language-Models

BradyFU/Awesome-Multimodal-Large-Language-Models

★ 18kpushed Jul 2, 2026

lmms-eval

EvolvingLMMs-Lab/lmms-eval

★ 4.3kpushed Jul 7, 2026

Tagline

Awesome-Multimodal-Large-Language-Models: :sparkles::sparkles:Latest Advances on Multimodal Large Language Models
lmms-eval: Unified Evaluation Toolkit for Multimodal Large Language Models

Stars

Awesome-Multimodal-Large-Language-Models: 18k
lmms-eval: 4.3k

Forks

Awesome-Multimodal-Large-Language-Models: 1.1k
lmms-eval: 614

Open issues

Awesome-Multimodal-Large-Language-Models: 104
lmms-eval: 43

Language

Awesome-Multimodal-Large-Language-Models: -
lmms-eval: Python

Adopt for

Awesome-Multimodal-Large-Language-Models: Awesome-Multimodal-Large-Language-Models curates the latest surveys, benchmarks, and advancements in multimodal large language models with a focus on evaluation methodologies and real-time vision/speech interaction. It's
lmms-eval: lmms-eval is a unified evaluation toolkit designed to assess multimodal large language models across various tasks including text, image, video, and audio with a focus on reproducibility and efficiency.

Persona

Awesome-Multimodal-Large-Language-Models: -
lmms-eval: -

Runtime

Awesome-Multimodal-Large-Language-Models: -
lmms-eval: -

License

Awesome-Multimodal-Large-Language-Models: -
lmms-eval: Other

Last pushed

Awesome-Multimodal-Large-Language-Models: Jul 2, 2026
lmms-eval: Jul 7, 2026

	Awesome-Multimodal-Large-Language-Models	lmms-eval
Tagline	:sparkles::sparkles:Latest Advances on Multimodal Large Language Models	Unified Evaluation Toolkit for Multimodal Large Language Models
Stars	18k	4.3k
Forks	1.1k	614
Open issues	104	43
Language	-	Python
Adopt for	Awesome-Multimodal-Large-Language-Models curates the latest surveys, benchmarks, and advancements in multimodal large language models with a focus on evaluation methodologies and real-time vision/speech interaction. It's	lmms-eval is a unified evaluation toolkit designed to assess multimodal large language models across various tasks including text, image, video, and audio with a focus on reproducibility and efficiency.
Persona	-	-
Runtime	-	-
License	-	Other
Last pushed	Jul 2, 2026	Jul 7, 2026
Categories	AI Agents, Evaluation & Observability, Model Training	Evaluation & Observability
Trust and health
Days since push	6d	2d
Open issues (now)	104	43
Owner type	User	Organization
Full report	Trust report	Trust report
Typed relationship	Awesome-Multimodal-Large-Language-Models related lmms-evalThe repository provides a list of resources regarding multmodal LLMs, which is the domain that lmms-eval aims to evaluate and improve upon, though it does not directly integrate or depend on this resource.

Choose Awesome-Multimodal-Large-Language-Models if…

Pricing: Not specified.
Requirements: The specific language and license are unspecified, indicating open access but caution is advised in using or reproducing the resources without verification..
The repository provides a list of resources regarding multmodal LLMs, which is the domain that lmms-eval aims to evaluate and improve upon, though it does not directly integrate or depend on this resource.
Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, large-vision-language-model, instruction-tuning, multimodal-chain-of-thought.
Also covers AI Agents, Model Training.
- You are working specifically within the domain of multimodal large language models and need up-to-date surveys, benchmarks, or projects.

When NOT to use Awesome-Multimodal-Large-Language-Models

- If your focus is on unimodal large language model training or evaluation where specific advancements in the multimodal domain are not required.
- You do not need real-time vision/speech interaction capabilities or the latest benchmarks in visual understanding as this repository tends to prioritize these advanced features.

GitHub Details

Choose lmms-eval if…

The repository provides a list of resources regarding multmodal LLMs, which is the domain that lmms-eval aims to evaluate and improve upon, though it does not directly integrate or depend on this resource.
Tags unique to lmms-eval: benchmark, multimodal-evaluation.
Use lmms-eval when you need a single, comprehensive solution for evaluating the performance of large language models (LLMs) in multiple modalities.

When NOT to use lmms-eval

Avoid using lmms-eval for single-modality evaluations where a narrower or more specialized toolkit could be more appropriate.
If reproducibility is not a primary concern in your model development workflow, then lmms-eval’s strict adherence to providing deterministic results through its unified pipeline may offer no clear优势。
如果你的评估流程不需要高性能和可信赖的结果，或者你的团队不需要支持多项任务和多个模型的统一工具，则不建议使用lmms-eval。它的高效性和信任度可能是其核心特点，但如果这些对于你的用例不是关键需求，那么它可能并不是最佳选择。

GitHub Details

Explore

Awesome-Multimodal-Large-Language-Models trust report →lmms-eval trust report →AI Agents category →Evaluation & Observability category →Model Training category →All comparisons Stack workflows Trending tools

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Common questions

What is the difference between Awesome-Multimodal-Large-Language-Models and lmms-eval?: Awesome-Multimodal-Large-Language-Models: :sparkles::sparkles:Latest Advances on Multimodal Large Language Models. lmms-eval: Unified Evaluation Toolkit for Multimodal Large Language Models. See the comparison table for live GitHub stats and shared categories.
When should I choose Awesome-Multimodal-Large-Language-Models over lmms-eval?: Choose Awesome-Multimodal-Large-Language-Models over lmms-eval when Pricing: Not specified; Requirements: The specific language and license are unspecified, indicating open access but caution is advised in using or reproducing the resources without verification.; The repository provides a list of resources regarding multmodal LLMs, which is the domain that lmms-eval aims to evaluate and improve upon, though it does not directly integrate or depend on this resource; Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, large-vision-language-model, instruction-tuning, multimodal-chain-of-thought; Also covers AI Agents, Model Training; - You are working specifically within the domain of multimodal large language models and need up-to-date surveys, benchmarks, or projects.
When should I choose lmms-eval over Awesome-Multimodal-Large-Language-Models?: Choose lmms-eval over Awesome-Multimodal-Large-Language-Models when The repository provides a list of resources regarding multmodal LLMs, which is the domain that lmms-eval aims to evaluate and improve upon, though it does not directly integrate or depend on this resource; Tags unique to lmms-eval: benchmark, multimodal-evaluation; Use lmms-eval when you need a single, comprehensive solution for evaluating the performance of large language models (LLMs) in multiple modalities.
When should I avoid Awesome-Multimodal-Large-Language-Models?: - If your focus is on unimodal large language model training or evaluation where specific advancements in the multimodal domain are not required. - You do not need real-time vision/speech interaction capabilities or the latest benchmarks in visual understanding as this repository tends to prioritize these advanced features.
When should I avoid lmms-eval?: Avoid using lmms-eval for single-modality evaluations where a narrower or more specialized toolkit could be more appropriate. If reproducibility is not a primary concern in your model development workflow, then lmms-eval’s strict adherence to providing deterministic results through its unified pipeline may offer no clear优势。如果你的评估流程不需要高性能和可信赖的结果，或者你的团队不需要支持多项任务和多个模型的统一工具，则不建议使用lmms-eval。它的高效性和信任度可能是其核心特点，但如果这些对于你的用例不是关键需求，那么它可能并不是最佳选择。
Is Awesome-Multimodal-Large-Language-Models or lmms-eval more popular on GitHub?: Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,930 vs 4,296). Stars measure visibility, not whether either tool fits your constraints.
Are Awesome-Multimodal-Large-Language-Models and lmms-eval open source?: Yes - both are open-source projects on GitHub.
Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or lmms-eval?: GraphCanon lists graph-backed alternatives at /tools/bradyfu-awesome-multimodal-large-language-models/alternatives and /tools/evolvinglmms-lab-lmms-eval/alternatives (/tools/bradyfu-awesome-multimodal-large-language-models/alternatives.md, /tools/evolvinglmms-lab-lmms-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 /compare/bradyfu-awesome-multimodal-large-language-models-vs-evolvinglmms-lab-lmms-eval.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, Awesome-Multimodal-Large-Language-Models or lmms-eval?: Awesome-Multimodal-Large-Language-Models: Very active. lmms-eval: 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-Multimodal-Large-Language-Models and lmms-eval?: GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Multimodal-Large-Language-Models: /tools/bradyfu-awesome-multimodal-large-language-models/trust; lmms-eval: /tools/evolvinglmms-lab-lmms-eval/trust.