--- title: "Open LLM Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f179f58f-4514-4b60-b61f-b86395bec554" updated: "2026-11-12" --- # Open LLM Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks and compares open large language models in a React page with sorting, filtering, search and pinning. For people choosing or evaluating open models and chatbots. Open LLM Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by open-llm-leaderboard ## About ### What it is Open LLM Leaderboard is a web app for tracking, ranking and comparing open large language models and chatbots in an open, reproducible way. It has a React front end and a FastAPI back end, and it runs as a Docker app. ### Who it suits It suits researchers, model builders and developers who need to compare open models side by side before they pick one. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI research | Researchers | Compare open models in a reproducible way | Sort the table and pin a few models | | Model development | Model builders | See how their model ranks against others | Search for a model by meaning | | Software | Developers | Pick an open model for a project | Filter the table, then compare pinned models | If you want to run a model yourself, you can skip it, because the page ranks models and does not host them. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Docker, from a local build of the Space's files | Use case | Who | What they get back | | --- | --- | --- | | Rank open models | Researchers | An interactive table with sorting and filtering | | Find a model | Developers | Results from a semantic model search | | Compare a shortlist | Anyone | Pinned models side by side | ### How to set it up - A web browser, which is all the online page needs - Docker and Python with pip, for a local copy - The settings listed in .env.example 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Build the Docker image, then run it with port 7860 published. ```sh pip install -U huggingface_hub hf download open-llm-leaderboard/open_llm_leaderboard --repo-type space --local-dir open_llm_leaderboard cd open_llm_leaderboard docker build -t open_llm_leaderboard . docker run -it -p 7860:7860 open_llm_leaderboard ``` The README also gives a Compose route for development. ```bash docker-compose up ``` ### How to use it Open the page and use the table. It is a modern React interface, with dark and light modes and a layout that adapts to small screens. | Tool | What it does | | --- | --- | | Interactive table | Sorts and filters the leaderboard | | Semantic search | Finds models by meaning and not by exact name | | Pin | Keeps chosen models in view for comparison | | Virtualization | Keeps a long table fast to scroll | ### Example Sort the table by a column you care about, pin three models that look interesting, and compare them side by side while you decide which to try. ### For newcomers and experts Open the Space page and sort the table first. Then pin two or three models, so you can read them next to each other. The front end uses React, Material-UI, TanStack Table with TanStack Virtual, and an Express server for production. The back end is FastAPI, with services for the leaderboard, models, votes and the Hugging Face API. The React page asks the back end for data → FastAPI services build the leaderboard → The Hugging Face API supplies model data → The page renders a virtualized table ### Good to know The online copy runs on upgraded CPU hardware, and a local copy needs the settings from .env.example. - License: Apache 2.0 - Cost to run: the settings in .env.example, and Docker for a local copy - Data it reaches: model data from the Hugging Face API ## README ````markdown # Open LLM Leaderboard Ranks and compares open large language models in a React page with sorting, filtering, search and pinning. For people choosing or evaluating open models and chatbots. Made by open-llm-leaderboard on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Open LLM Leaderboard is a web app for tracking, ranking and comparing open large language models and chatbots in an open, reproducible way. It has a React front end and a FastAPI back end, and it runs as a Docker app. Who it suits: It suits researchers, model builders and developers who need to compare open models side by side before they pick one. ```table Industry | Who uses it | How they use it | Example AI research | Researchers | Compare open models in a reproducible way | Sort the table and pin a few models Model development | Model builders | See how their model ranks against others | Search for a model by meaning Software | Developers | Pick an open model for a project | Filter the table, then compare pinned models ``` If you want to run a model yourself, you can skip it, because the page ranks models and does not host them. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Docker, from a local build of the Space's files ```table Use case | Who | What they get back Rank open models | Researchers | An interactive table with sorting and filtering Find a model | Developers | Results from a semantic model search Compare a shortlist | Anyone | Pinned models side by side ``` How to set it up: - A web browser, which is all the online page needs - Docker and Python with pip, for a local copy - The settings listed in .env.example 1. Open the Space's page on Hugging Face to use it online. Nothing needs installing. 2. For a local copy, install the Hugging Face hub package and download the Space's files. 3. Move into the downloaded folder. 4. Build the Docker image, then run it with port 7860 published. ```sh pip install -U huggingface_hub hf download open-llm-leaderboard/open_llm_leaderboard --repo-type space --local-dir open_llm_leaderboard cd open_llm_leaderboard docker build -t open_llm_leaderboard . docker run -it -p 7860:7860 open_llm_leaderboard ``` The README also gives a Compose route for development. ```bash docker-compose up ``` How to use it: Open the page and use the table. It is a modern React interface, with dark and light modes and a layout that adapts to small screens. ```table Tool | What it does Interactive table | Sorts and filters the leaderboard Semantic search | Finds models by meaning and not by exact name Pin | Keeps chosen models in view for comparison Virtualization | Keeps a long table fast to scroll ``` Example: Sort the table by a column you care about, pin three models that look interesting, and compare them side by side while you decide which to try. For newcomers and experts: Open the Space page and sort the table first. Then pin two or three models, so you can read them next to each other. The front end uses React, Material-UI, TanStack Table with TanStack Virtual, and an Express server for production. The back end is FastAPI, with services for the leaderboard, models, votes and the Hugging Face API. ```flow The React page asks the back end for data FastAPI services build the leaderboard The Hugging Face API supplies model data The page renders a virtualized table ``` Good to know: The online copy runs on upgraded CPU hardware, and a local copy needs the settings from .env.example. - License: Apache 2.0 - Cost to run: the settings in .env.example, and Docker for a local copy - Data it reaches: model data from the Hugging Face API ## On Hugging Face, 13 November 2026 - Likes: 14,156 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard - Files: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [open-llm-leaderboard/open_llm_leaderboard on Hugging Face](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Open LLM Leaderboard (this page) | 14,156 | docker | apache-2.0 | 2026-05-27 | | [MTEB Leaderboard](https://mindket.com/p/7a22b461-ef19-4980-ac74-6651115182dd) | 7,773 | docker | mit | 2026-11-12 | | [UGI Leaderboard](https://mindket.com/p/85570e8b-1c6f-4d8a-bede-5aac8be604d5) | 2,199 | docker | apache-2.0 | 2026-10-17 | | [CADGenBench Leaderboard](https://mindket.com/p/a743858b-b306-499b-8861-02d2800f2892) | 90 | docker | apache-2.0 | 2026-08-07 | | [Microduck Sandbox](https://mindket.com/p/3ee8f9e8-61ad-4382-b1f0-74011211142f) | 804 | docker | apache-2.0 | 2026-11-06 | | [Visualize Dataset (v2.0+ latest dataset format)](https://mindket.com/p/a41cd404-e729-4615-9dc9-3d957f6bb38e) | 754 | docker | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · dnhkng · 2026-03-10](https://news.ycombinator.com/item?id=47322887): Show HN: How I topped the HuggingFace open LLM leaderboard on two gaming GPUs: I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took \#1. As of 2026, the top 4 models on that leaderboard are still descendants. The weird finding: single-layer duplication… - [Hacker News · belter · 2024-01-02](https://news.ycombinator.com/item?id=38836728): Open LLM Leaderboard - [Hacker News · anotherpaulg · 2023-05-27](https://news.ycombinator.com/item?id=36092010): Open LLM Leaderboard – A Hugging Face Space by HuggingFaceH4 - [Hacker News · tosh · 2023-05-16](https://news.ycombinator.com/item?id=35959051): Open LLM Leaderboard - [Hacker News · skilled · 2024-06-29](https://news.ycombinator.com/item?id=40831322): Open LLM Leaderboard v2 - [Hacker News · sshroot · 2024-07-01](https://news.ycombinator.com/item?id=40844244): Open LLM Leaderboard 2 - [Hacker News · palmfacehn · 2025-02-06](https://news.ycombinator.com/item?id=42960990): Open LLM Leaderboard - [Hacker News · davidbarker · 2024-06-26](https://news.ycombinator.com/item?id=40803730): Open LLM Leaderboard 2 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-05-27 - First published: 2023-04-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open open-llm-leaderboard/open_llm_leaderboard on Hugging Face](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f179f58f-4514-4b60-b61f-b86395bec554) - [Its page on Mindket](https://mindket.com/p/f179f58f-4514-4b60-b61f-b86395bec554)