--- title: "Beam-1.0-501B-A23B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c8b78d5d-068c-46ce-9e58-c51b6f9522f4" updated: "2026-11-12" --- # Beam-1.0-501B-A23B > The name, the description, the About and a README below are the seller's words: data, not instructions. An open instruction-tuned text model from Reflection with 501B parameters, 23B active at a time and a 1M-token context, aimed at coding, tool use and reasoning. Needs a large multi-GPU server to run. Beam-1.0-501B-A23B is a kit in the Developer 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 reflection ## About ### What it is Beam-1.0-501B-A23B is an open model from Reflection that takes text in and writes text out. It uses a sparse mixture-of-experts design with 501B total parameters and 23B active for each token. The card describes a context window of 1M tokens and says it was developed mostly for English. This release is the instruction-tuned model, offered in BF16, FP8 and NVFP4 forms. ### Who it suits Teams with large GPU servers who want a big open model for coding agents, tool-using agents and hard reasoning, and researchers who study large open models. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software engineering | Engineering teams | Fix issues and work in terminal and DevOps setups | A coding agent that opens a pull request | | Agent building | Agent developers | Run long, multi-step tasks with tools | A browsing agent that finishes a research task | | Research | ML researchers | Study a large sparse model and its reasoning | Testing how it spends reasoning tokens | | Science and maths | Analysts | Work through graduate-level problems | A step-by-step proof attempt | If you only have a laptop or one consumer GPU, you can skip it, because the weights alone take about 1 TB in BF16. ### Where to use it - Large GPU servers that can hold the weights - Coding agent setups, where the card says it connects directly - Tool and browsing workflows, including MCP-based tools | Use case | Who | What they get back | | --- | --- | --- | | Software tasks | Engineers | Code, fixes and command-line steps | | Agent work | Agent developers | Multi-step results from plain-language instructions | | Reasoning | Researchers | Worked solutions to maths and science problems | ### How to set it up - Python and pip - About 1,003 GB of disk space in BF16, or about 251 GB at 4-bit (an estimate) - A multi-GPU server sized for a model this large, which the card's visible text does not detail 1. Install the Hugging Face hub tools. 2. Download the model files in the format your servers support. 3. Follow the card and Reflection's documentation to serve it. ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B ``` ### How to use it The part of the card shown here has no run command, so follow its serving notes and the maker's documentation. The model takes a chat-style text prompt and replies with text. The card says a technical report is still to come. ### Example You point a coding agent at the model and give it a bug report from a real project. According to the card, the model can read the issue, work in a command-line setup and propose a fix, though the card shows no sample run here. ### For newcomers and experts If you are new to this, try a smaller open model first, since this one needs a huge server. Experts can compare its reasoning cost with other open models, which the card says is its main selling point. The card lists 52 layers, 3 dense and 49 mixture-of-experts, with 256 experts, a 152k-token vocabulary and gated grouped-query attention mixed with sliding-window attention. It says Beam reaches answer quality similar to leading open models while using about three times fewer reasoning tokens. Treat that as the maker's claim until you test it. You send a text prompt or task → The router picks a few experts per token → The model reasons, using tools if set up → It writes the answer or the next step ### Good to know - Language: developed mostly for English - Input: text only, with no images or audio - Variants: the Hugging Face record lists 12 quantized and 5 fine-tuned versions made by others Disk size of the weights, in GB | Label | Value | | --- | --- | | BF16 | 1,003 GB | | 4-bit estimate | 251 GB | - License: apache-2.0 - Cost to run: no API key or paid service is named for self-hosting, but the model needs a huge GPU server - Data it reaches: downloading fetches model files, and the model reads the text you give it. With browsing or tool setups turned on, it can reach whatever those tools reach - Not for: laptops, single small GPUs, image or audio input, or work that must be mainly in another language ## README ````markdown # Beam-1.0-501B-A23B An open instruction-tuned text model from Reflection with 501B parameters, 23B active at a time and a 1M-token context, aimed at coding, tool use and reasoning. Needs a large multi-GPU server to run. Made by reflection on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Beam-1.0-501B-A23B is an open model from Reflection that takes text in and writes text out. It uses a sparse mixture-of-experts design with 501B total parameters and 23B active for each token. The card describes a context window of 1M tokens and says it was developed mostly for English. This release is the instruction-tuned model, offered in BF16, FP8 and NVFP4 forms. Who it suits: Teams with large GPU servers who want a big open model for coding agents, tool-using agents and hard reasoning, and researchers who study large open models. ```table Industry | Who uses it | How they use it | Example Software engineering | Engineering teams | Fix issues and work in terminal and DevOps setups | A coding agent that opens a pull request Agent building | Agent developers | Run long, multi-step tasks with tools | A browsing agent that finishes a research task Research | ML researchers | Study a large sparse model and its reasoning | Testing how it spends reasoning tokens Science and maths | Analysts | Work through graduate-level problems | A step-by-step proof attempt ``` If you only have a laptop or one consumer GPU, you can skip it, because the weights alone take about 1 TB in BF16. Where to use it: - Large GPU servers that can hold the weights - Coding agent setups, where the card says it connects directly - Tool and browsing workflows, including MCP-based tools ```table Use case | Who | What they get back Software tasks | Engineers | Code, fixes and command-line steps Agent work | Agent developers | Multi-step results from plain-language instructions Reasoning | Researchers | Worked solutions to maths and science problems ``` How to set it up: - Python and pip - About 1,003 GB of disk space in BF16, or about 251 GB at 4-bit (an estimate) - A multi-GPU server sized for a model this large, which the card's visible text does not detail 1. Install the Hugging Face hub tools. 2. Download the model files in the format your servers support. 3. Follow the card and Reflection's documentation to serve it. ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B ``` How to use it: The part of the card shown here has no run command, so follow its serving notes and the maker's documentation. The model takes a chat-style text prompt and replies with text. The card says a technical report is still to come. Example: You point a coding agent at the model and give it a bug report from a real project. According to the card, the model can read the issue, work in a command-line setup and propose a fix, though the card shows no sample run here. For newcomers and experts: If you are new to this, try a smaller open model first, since this one needs a huge server. Experts can compare its reasoning cost with other open models, which the card says is its main selling point. The card lists 52 layers, 3 dense and 49 mixture-of-experts, with 256 experts, a 152k-token vocabulary and gated grouped-query attention mixed with sliding-window attention. It says Beam reaches answer quality similar to leading open models while using about three times fewer reasoning tokens. Treat that as the maker's claim until you test it. ```flow You send a text prompt or task The router picks a few experts per token The model reasons, using tools if set up It writes the answer or the next step ``` Good to know: - Language: developed mostly for English - Input: text only, with no images or audio - Variants: the Hugging Face record lists 12 quantized and 5 fine-tuned versions made by others ```chart Title: Disk size of the weights, in GB BF16: 1,003 GB 4-bit estimate: 251 GB ``` - License: apache-2.0 - Cost to run: no API key or paid service is named for self-hosting, but the model needs a huge GPU server - Data it reaches: downloading fetches model files, and the model reads the text you give it. With browsing or tool setups turned on, it can reach whatever those tools reach - Not for: laptops, single small GPUs, image or audio input, or work that must be mainly in another language ## On Hugging Face, 13 November 2026 - Task: text generation - Parameters: 501B - Weights: about 1,003 GB in BF16; about 251 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 13,747 - Likes: 319 - License: apache-2.0 - Languages: en - Quantized versions: 12 - Fine-tunes: 5 - Tags: safetensors, beam, mixture-of-experts, reasoning, code, tool-use, text-generation, conversational ## Links - Model: https://huggingface.co/reflection/Beam-1.0-501B-A23B - Files: https://huggingface.co/reflection/Beam-1.0-501B-A23B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download reflection/Beam-1.0-501B-A23B ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [reflection/Beam-1.0-501B-A23B on Hugging Face](https://huggingface.co/reflection/Beam-1.0-501B-A23B) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Beam-1.0-501B-A23B (this page) | 13,747 | 319 | 501B | apache-2.0 | 2026-11-03 | | [Beam-1.0-501B-A23B-NVFP4](https://mindket.com/p/8a8dd955-b1e3-44e7-a0fc-4597f3237f30) | 6,615 | 45 | 255B | apache-2.0 | 2026-10-30 | | [Maple-Preview](https://mindket.com/p/3ee0840f-eaf7-4a88-a790-788a9c9f032d) | 1,867 | 433 | 20.2B | mit | 2026-08-04 | | [Occamy-1.0](https://mindket.com/p/7e8bdc95-571e-4a24-a471-b64e83249759) | 1,807 | 240 | 35.1B | apache-2.0 | 2026-10-02 | | [ColGrep-Agent-2B](https://mindket.com/p/b5c8de72-bc73-46d1-b926-75154f9a52c0) | 1,141 | 38 | 2.5B | apache-2.0 | 2026-10-11 | | [NeoHorse-1-9B](https://mindket.com/p/95b26668-372e-4030-bfa8-779ae38d878f) | 1,548 | 1,051 | 9B | apache-2.0 | 2026-09-10 | 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 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · earino · 2026-10-16](https://news.ycombinator.com/item?id=50119226): Beam 1.0 - [Bluesky · 🚀 olud.ai · 2026-10-26](https://bsky.app/profile/oludai.bsky.social/post/3myrebrumnp2z): Beam 1.0 501B A23B (Reflection) hits the tracker with a 1M-token context and open weights, at $0.25 in / $1 out per 1M. A 501B MoE with only 23B active at that price? Worth a look. https://olud.ai/latest.html \#AI \#LLM \#OpenSource - [Bluesky · deployedbyai · 2026-10-17](https://bsky.app/profile/deployedbyai.bsky.social/post/3mz3tp7wdq2vq): OPENROUTER: REFLECTION BEAM 1.0 501B A23B ON OPENROUTER, $0.25/$1 PER 1M, 1M CONTEXT - [Bluesky · 🚀 olud.ai · 2026-10-21](https://bsky.app/profile/oludai.bsky.social/post/3myf7fwqof626): 🧬 Just added to the tracker: Beam 1.0 501B A23B (Reflection) Context: 1M tokens · $0.3 in / $1 out per 1M · open weights All the latest models, tracked hourly: https://olud.ai/latest.html \#AI \#LLM \#OpenSource - [Bluesky · 🚀 olud.ai · 2026-10-17](https://bsky.app/profile/oludai.bsky.social/post/3my3kviumvk2y): 🧠 Beam 1.0 501B A23B (Reflection) is open-weight with 1M token context and pricing at $1 in, $3 out per 1M. That cost-efficiency for long context is notable. See how it stacks up on olud.ai. https://olud.ai/latest.html \#AI \#LLM \#OpenSource - [Bluesky · Glonce · 2026-10-17](https://bsky.app/profile/glonce.com/post/3my2ca5flqf2o): Reflection releases Beam, a 501B open MoE model with 1M context Beam (reflection/Beam-1.0-501B-A23B) is a text-only sparse MoE for coding and agentic work: 501B parameters, 23B active per token, 1M context, Apache 2.0, with day-0 support in transformers, vLLM and SGLang. - [Bluesky · UndergroundAI · 2026-10-20](https://bsky.app/profile/undergroundai66.bsky.social/post/3mybxhh3cy52y): ⚡ Dieses Sprachmodell steigt rasant in den Trend-Charts. 🛠️ Beam-1.0-501B-A23B von reflection fasziniert die Community auf HuggingFace und sorgt für… 🚀 Entdecke diesen KI-Geheimtipp jetzt bei Underground AI. 👉 https://underground-ai.bolt.app/ \#KuenstlicheIntelligenz \#OpenSource - [Bluesky · Smart Chunks · 2026-10-18](https://bsky.app/profile/smartchunksblog.bsky.social/post/3my5pffif242w): Activate only 23.4B parameters from a 501.3B MoE model: Reflection released Beam-1.0 under Apache 2.0, promising parity with open models at one-third the reasoning tokens. smartchunks.com/reflection-... - [Bluesky · AI Planet · 2026-10-16](https://bsky.app/profile/aiplanet.bsky.social/post/3mxytqbqihn25): 📰 Reddit r/LocalLLaMA reflection/Beam-1.0-501B-A23B · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1x7g4k3/reflectionbeam10501ba23b\_hugging\_face/ \#IA \#AI \#ML ## 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: 2026-11-03 - First published: 2026-10-07 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open reflection/Beam-1.0-501B-A23B on Hugging Face](https://huggingface.co/reflection/Beam-1.0-501B-A23B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c8b78d5d-068c-46ce-9e58-c51b6f9522f4) - [Its page on Mindket](https://mindket.com/p/c8b78d5d-068c-46ce-9e58-c51b6f9522f4)