--- title: "Awesome-Multimodal-Large-Language-Models vs helm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-stanford-crfm-helm" tools: ["bradyfu-awesome-multimodal-large-language-models", "stanford-crfm-helm"] --- # Awesome-Multimodal-Large-Language-Models vs helm *GraphCanon updated Jul 12, 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 helm if helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. [Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) reports 18k GitHub stars, 1.1k forks, and 104 open issues, last pushed Jul 2, 2026. [helm](https://crfm.stanford.edu/helm) has 2.9k stars, 400 forks, and 84 open issues, last pushed Jul 1, 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 [helm's repository](https://github.com/stanford-crfm/helm). | | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Tagline | Latest Advances on Multimodal Large Language Models | Holistic, reproducible and transparent evaluation of foundation models | | Stars | 17,937 | 2,850 | | Forks | 1,129 | 400 | | Open issues | 104 | 84 | | 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 | Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | LLM Frameworks, Evaluation & Observability | 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) | [helm](/tools/stanford-crfm-helm.md) | | --- | --- | --- | | Days since push | 8d | 10d | | Open issues (now) | 104 | 84 | | Owner type | User | Organization | | Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/stanford-crfm-helm/trust.md) | ## 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: helm - **Adopt for:** Helm is an open-source Python framework for evaluating foundation models, including LLMs and multimodal models. It emphasizes holistic, reproducible, and transparent evaluation processes. ## Choose when ### Choose Awesome-Multimodal-Large-Language-Models if… - Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, instruction-tuning, multi-modality, large-language-models. - Also covers LLM Frameworks. - - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. ### Choose helm if… - Tags unique to helm: evaluation, language-models, foundation models, framework. - When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way. - Leaner open-issue backlog (84). ## 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 helm - Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. - If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ## Common questions ### What is the difference between Awesome-Multimodal-Large-Language-Models and helm? Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal Large Language Models. helm: Holistic, reproducible and transparent evaluation of foundation models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Multimodal-Large-Language-Models over helm? Choose Awesome-Multimodal-Large-Language-Models over helm when Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, instruction-tuning, multi-modality, large-language-models; 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 helm over Awesome-Multimodal-Large-Language-Models? Choose helm over Awesome-Multimodal-Large-Language-Models when Tags unique to helm: evaluation, language-models, foundation models, framework; When you need a comprehensive tool to evaluate the performance of large language models (LLMs) and other types of foundation models in a standardized way; Leaner open-issue backlog (84). ### 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 helm? Helm may not be suitable if you are working with smaller scale projects that do not require extensive, holistic evaluation capabilities associated with foundation models. If your framework of choice already provides sufficient evaluation tools or processes for foundation models, adding Helm might introduce unnecessary complexity. ### Is Awesome-Multimodal-Large-Language-Models or helm more popular on GitHub? Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,937 vs 2,850). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Multimodal-Large-Language-Models and helm open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or helm? GraphCanon lists graph-backed alternatives at [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) and [helm alternatives](/tools/stanford-crfm-helm/alternatives) ([Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives.md), [helm markdown twin](/tools/stanford-crfm-helm/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-stanford-crfm-helm.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 helm? Awesome-Multimodal-Large-Language-Models: Active. helm: 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 helm? 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); [helm trust report](/tools/stanford-crfm-helm/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/_