--- title: "Awesome-Multimodal-Large-Language-Models vs VLMEvalKit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-open-compass-vlmevalkit" tools: ["bradyfu-awesome-multimodal-large-language-models", "open-compass-vlmevalkit"] --- # Awesome-Multimodal-Large-Language-Models vs VLMEvalKit *GraphCanon updated Aug 17, 2026* ## Verdict Pick Awesome-Multimodal-Large-Language-Models if awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking; 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-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) reports 18k GitHub stars, 1.1k forks, and 111 open issues, last pushed Aug 14, 2026. [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-Multimodal-Large-Language-Models's repository](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) and [VLMEvalKit's repository](https://github.com/open-compass/VLMEvalKit). | | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) | | --- | --- | --- | | Tagline | Latest Advances on Multimodal Large Language Models | An open-source evaluation toolkit for large vision-language models | | Stars | 17,978 | 4,345 | | Forks | 1,133 | 745 | | Open issues | 111 | 285 | | Language | - | Python | | Adopt for | Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking | 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 | - | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | 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) | [VLMEvalKit](/tools/open-compass-vlmevalkit.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 111 | 285 | | Stars delta | +29 (30d) | +60 (30d) | | Open issues delta | +4 (30d) | +21 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/open-compass-vlmevalkit/trust.md) | **Typed relationship:** Awesome-Multimodal-Large-Language-Models _(related)_ VLMEvalKit VLMEvalKit focuses on evaluating multimodal large language models, and Awesome-Multimodal-Large-Language-Models provides a curated list of such models and resources. They are related but serve different purposes. ## Decision facts: Awesome-Multimodal-Large-Language-Models - **Adopt for:** Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking ## 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-Multimodal-Large-Language-Models if… - VLMEvalKit focuses on evaluating multimodal large language models, and Awesome-Multimodal-Large-Language-Models provides a curated list of such models and resources. They are related but serve different purposes. - Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-following, instruction-tuning. - Also covers LLM Frameworks. - - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. ### Choose VLMEvalKit if… - VLMEvalKit focuses on evaluating multimodal large language models, and Awesome-Multimodal-Large-Language-Models provides a curated list of such models and resources. They are related but serve different purposes. - Tags unique to VLMEvalKit: computer-vision, evaluation, 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-Multimodal-Large-Language-Models - - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ## 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-Multimodal-Large-Language-Models and VLMEvalKit? Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal 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-Multimodal-Large-Language-Models over VLMEvalKit? Choose Awesome-Multimodal-Large-Language-Models over VLMEvalKit when VLMEvalKit focuses on evaluating multimodal large language models, and Awesome-Multimodal-Large-Language-Models provides a curated list of such models and resources. They are related but serve different purposes; Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-following, instruction-tuning; Also covers LLM Frameworks; - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. ### When should I choose VLMEvalKit over Awesome-Multimodal-Large-Language-Models? Choose VLMEvalKit over Awesome-Multimodal-Large-Language-Models when VLMEvalKit focuses on evaluating multimodal large language models, and Awesome-Multimodal-Large-Language-Models provides a curated list of such models and resources. They are related but serve different purposes; Tags unique to VLMEvalKit: computer-vision, evaluation, 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-Multimodal-Large-Language-Models? - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ### 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-Multimodal-Large-Language-Models or VLMEvalKit more popular on GitHub? Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,978 vs 4,345). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Multimodal-Large-Language-Models and VLMEvalKit open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or VLMEvalKit? GraphCanon lists graph-backed alternatives at [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) and [VLMEvalKit alternatives](/tools/open-compass-vlmevalkit/alternatives) ([Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/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/bradyfu-awesome-multimodal-large-language-models-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-Multimodal-Large-Language-Models or VLMEvalKit? Awesome-Multimodal-Large-Language-Models: Very active. 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-Multimodal-Large-Language-Models and VLMEvalKit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Multimodal-Large-Language-Models trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust); [VLMEvalKit trust report](/tools/open-compass-vlmevalkit/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/_