--- title: "Awesome-Multimodal-Large-Language-Models vs LeanEuclid" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bradyfu-awesome-multimodal-large-language-models-vs-loganrjmurphy-leaneuclid" tools: ["bradyfu-awesome-multimodal-large-language-models", "loganrjmurphy-leaneuclid"] --- # Awesome-Multimodal-Large-Language-Models vs LeanEuclid *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 LeanEuclid if decision-relevant specifics for LeanEuclid, a benchmark tailored for autoformalization in Euclidean geometry within the Lean proof assistant ecosystem. [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. [LeanEuclid](http://arxiv.org/abs/2405.17216) has 139 stars, 17 forks, and 5 open issues, last pushed Nov 25, 2025. Figures are from public GitHub metadata via [Awesome-Multimodal-Large-Language-Models's repository](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) and [LeanEuclid's repository](https://github.com/loganrjmurphy/LeanEuclid). | | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | [LeanEuclid](/tools/loganrjmurphy-leaneuclid.md) | | --- | --- | --- | | Tagline | Latest Advances on Multimodal Large Language Models | Benchmark for autoformalization in Euclidean geometry targeting Lean proof assistant. | | Stars | 17,978 | 139 | | Forks | 1,133 | 17 | | Open issues | 111 | 5 | | Language | - | Lean | | 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-relevant specifics for LeanEuclid, a benchmark tailored for autoformalization in Euclidean geometry within the Lean proof assistant ecosystem. | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | 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) | [LeanEuclid](/tools/loganrjmurphy-leaneuclid.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 245d | | Open issues (now) | 111 | 5 | | Stars delta | +29 (30d) | Unknown | | Open issues delta | +4 (30d) | Unknown | | Full report | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/trust.md) | [trust report](/tools/loganrjmurphy-leaneuclid/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: LeanEuclid - **Requirements:** Requires a fully functional setup with Lean 4, including elan and Lean's VSCode extension; Installation of Z3 and CVC5 solvers is mandatory for effective use; Python dependencies such as `smt-portfolio` and `openai` need to be installed via pip; Setting up server environment paths in Lean’s VSCode extension correctly is essential for tool functionality - **Adopt for:** Decision-relevant specifics for LeanEuclid, a benchmark tailored for autoformalization in Euclidean geometry within the Lean proof assistant ecosystem. ## Choose when ### Choose Awesome-Multimodal-Large-Language-Models if… - 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 LeanEuclid if… - Requirements: Requires a fully functional setup with Lean 4, including elan and Lean's VSCode extension; Installation of Z3 and CVC5 solvers is mandatory for effective use; Python dependencies such as `smt-portfolio` and `openai` need to be installed via pip; Setting up server environment paths in Lean’s VSCode extension correctly is essential for tool functionality. - Tags unique to LeanEuclid: autoformalization, euclidean-geometry, formalization, lean4. - LeanEuclid ships Docker support for self-hosted deployment. - When you are specifically interested in advancing or testing automated theorem proving and formal verification techniques in Euclidean geometry using Lean 4 ## 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 LeanEuclid - Avoid if you are working within a different proof assistant ecosystem unrelated to Lean 4 - Not suitable for benchmarking or developing autoformalization techniques outside the domain of Euclidean geometry ## Common questions ### What is the difference between Awesome-Multimodal-Large-Language-Models and LeanEuclid? Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal Large Language Models. LeanEuclid: Benchmark for autoformalization in Euclidean geometry targeting Lean proof assistant.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Multimodal-Large-Language-Models over LeanEuclid? Choose Awesome-Multimodal-Large-Language-Models over LeanEuclid when 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 LeanEuclid over Awesome-Multimodal-Large-Language-Models? Choose LeanEuclid over Awesome-Multimodal-Large-Language-Models when Requirements: Requires a fully functional setup with Lean 4, including elan and Lean's VSCode extension; Installation of Z3 and CVC5 solvers is mandatory for effective use; Python dependencies such as `smt-portfolio` and `openai` need to be installed via pip; Setting up server environment paths in Lean’s VSCode extension correctly is essential for tool functionality; Tags unique to LeanEuclid: autoformalization, euclidean-geometry, formalization, lean4; LeanEuclid ships Docker support for self-hosted deployment; When you are specifically interested in advancing or testing automated theorem proving and formal verification techniques in Euclidean geometry using Lean 4. ### 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 LeanEuclid? Avoid if you are working within a different proof assistant ecosystem unrelated to Lean 4 Not suitable for benchmarking or developing autoformalization techniques outside the domain of Euclidean geometry ### Is Awesome-Multimodal-Large-Language-Models or LeanEuclid more popular on GitHub? Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,978 vs 139). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Multimodal-Large-Language-Models and LeanEuclid open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Multimodal-Large-Language-Models or LeanEuclid? GraphCanon lists graph-backed alternatives at [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) and [LeanEuclid alternatives](/tools/loganrjmurphy-leaneuclid/alternatives) ([Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives.md), [LeanEuclid markdown twin](/tools/loganrjmurphy-leaneuclid/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-loganrjmurphy-leaneuclid.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 LeanEuclid? Awesome-Multimodal-Large-Language-Models: Very active. LeanEuclid: Slowing. 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 LeanEuclid? 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); [LeanEuclid trust report](/tools/loganrjmurphy-leaneuclid/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/_