---
title: "Awesome-Multimodal-Large-Language-Models vs lmms-eval"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-evolvinglmms-lab-lmms-eval"
tools: ["bradyfu-awesome-multimodal-large-language-models", "evolvinglmms-lab-lmms-eval"]
---

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

Neutral, constraint-first comparison with live GitHub stats.

| | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) |
| --- | --- | --- |
| Tagline | :sparkles::sparkles:Latest Advances on Multimodal Large Language Models | Unified Evaluation Toolkit for Multimodal Large Language Models |
| Stars | 17,930 | 4,296 |
| Forks | 1,129 | 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 |
| Categories | Evaluation & Observability, AI Agents, Model Training | Evaluation & Observability |

## Trust and health

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

| | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) |
| --- | --- | --- |
| Days since push | 6d | 2d |
| Open issues (now) | 104 | 43 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/evolvinglmms-lab-lmms-eval/trust.md) |

**Typed relationship:** Awesome-Multimodal-Large-Language-Models _(related)_ lmms-eval

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.

## Decision facts: Awesome-Multimodal-Large-Language-Models

- **Pricing:** unknown - 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.
- **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

## Decision facts: lmms-eval

- **Adopt for:** 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.

## Choose when

### 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.

### 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 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 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。

## 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.

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models`](/api/graphcanon/graph?tool=bradyfu-awesome-multimodal-large-language-models)
- 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/_