--- title: "Nanbeige4.3-33B" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c7975e8b-eb6f-497a-a398-2028206aa2a0" updated: "2026-11-12" --- # Nanbeige4.3-33B > The name, the description, the About and a README below are the seller's words: data, not instructions. A 33.1B open model for agentic coding and professional cowork tasks, built as the flagship teacher of the Nanbeige4.3 series. For developers and agent builders who want a strong open model near 30B parameters. Nanbeige4.3-33B 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 Nanbeige ## About ### What it is Nanbeige4.3-33B is a text generation model with 33.1B parameters. The authors built it as the flagship teacher of the Nanbeige4.3 series, to carry agent skills into smaller models later. It targets complex coding and professional cowork tasks, and it speaks English and Chinese. ### Who it suits It suits developers and agent builders who want an open model of about 30B parameters for coding agents and office-style task automation. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Run a coding agent on a repository | Fix a bug found by a failing test | | Office automation | Operations teams | Automate multi-step professional tasks | A task that touches several tools in a row | | AI research | Researchers | Study teacher models for distillation | Use it as the source for a smaller student model | | Enterprise IT | Platform teams | Host an open model for internal agents | An internal assistant that plans and calls tools | If you need a small model for a phone or laptop, you can skip it, since the card plans a smaller 3B model for that. ### Where to use it - Python with the transformers library - Agent setups for coding and tool use, such as the ones the card used for its tests - Windows, macOS or Linux machines with enough GPU memory for a 33B model | Use case | Who | What they get back | | --- | --- | --- | | Agentic coding | Developers | Code changes and answers about a repository | | Professional cowork tasks | Operations teams | Planned, multi-step work done through tools | | Student model teaching | Researchers | A teacher whose outputs can train smaller models | ### How to set it up You need these first: - Python and pip - A GPU setup with room for about 66.2 GB of BF16 weights, or about 16.6 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download Nanbeige/Nanbeige4.3-33B ``` ### How to use it Chat with it through the model's chat template, or wire it into an agent. The card's tests ran it at its highest thinking effort with a chat template setting that keeps the thinking text. | Tool | What it does | | --- | --- | | preserve\_thinking=true | A chat template setting that keeps the model's thinking text, used in the card's tests | | Highest thinking effort | The mode in which the card compared all models | ### Example You ask it to fix a failing test in a project through your coding agent. The model reads the code, plans the change, calls tools and returns a patch. Its Terminal-Bench 2.1 result in the card is 80.9. ### For newcomers and experts This is a large model for building agents. It is not a quick download, so a good first step is to read the model card and decide whether your GPU can hold the 4-bit version. The card compares it with three rivals of similar size, Gemma4-31B, Qwen3.8-27B and Muse-Glimmer-30B, with the same inference settings for each. It leads on most agentic coding and cowork tables, but not on SWE-Bench Pro, GDPval or GPQA Diamond. You give the agent a task → The model plans and thinks → It calls tools and reads results → It returns code or a finished answer ### Good to know These are the authors' own scores, and each benchmark has its own setup noted in the card. Terminal-Bench 2.1 score | Label | Value | | --- | --- | | Nanbeige4.3-33B | 80.9 | | Qwen3.8-27B | 73.0 | | Muse-Glimmer-30B | 61.8 | | Gemma4-31B | 47.2 | - Where it trails: SWE-Bench Pro 60.3 against 61.7 for Qwen3.8-27B, GDPval 84.8 against 86.4, and GPQA Diamond 89.5 against 91.3 for Gemma4-31B - Teacher role: the authors say it will feed a later distillation pipeline for smaller models such as Nanbeige4.3-3B - Other versions: 101 quantized and 11 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and any files or tools you connect to it ## README ````markdown # Nanbeige4.3-33B A 33.1B open model for agentic coding and professional cowork tasks, built as the flagship teacher of the Nanbeige4.3 series. For developers and agent builders who want a strong open model near 30B parameters. Made by Nanbeige 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: Nanbeige4.3-33B is a text generation model with 33.1B parameters. The authors built it as the flagship teacher of the Nanbeige4.3 series, to carry agent skills into smaller models later. It targets complex coding and professional cowork tasks, and it speaks English and Chinese. Who it suits: It suits developers and agent builders who want an open model of about 30B parameters for coding agents and office-style task automation. ```table Industry | Who uses it | How they use it | Example Software | Developers | Run a coding agent on a repository | Fix a bug found by a failing test Office automation | Operations teams | Automate multi-step professional tasks | A task that touches several tools in a row AI research | Researchers | Study teacher models for distillation | Use it as the source for a smaller student model Enterprise IT | Platform teams | Host an open model for internal agents | An internal assistant that plans and calls tools ``` If you need a small model for a phone or laptop, you can skip it, since the card plans a smaller 3B model for that. Where to use it: - Python with the transformers library - Agent setups for coding and tool use, such as the ones the card used for its tests - Windows, macOS or Linux machines with enough GPU memory for a 33B model ```table Use case | Who | What they get back Agentic coding | Developers | Code changes and answers about a repository Professional cowork tasks | Operations teams | Planned, multi-step work done through tools Student model teaching | Researchers | A teacher whose outputs can train smaller models ``` How to set it up: You need these first: - Python and pip - A GPU setup with room for about 66.2 GB of BF16 weights, or about 16.6 GB at a 4-bit estimate 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load the model with transformers as the model card describes. ```sh pip install -U huggingface_hub hf download Nanbeige/Nanbeige4.3-33B ``` How to use it: Chat with it through the model's chat template, or wire it into an agent. The card's tests ran it at its highest thinking effort with a chat template setting that keeps the thinking text. ```table Tool | What it does preserve_thinking=true | A chat template setting that keeps the model's thinking text, used in the card's tests Highest thinking effort | The mode in which the card compared all models ``` Example: You ask it to fix a failing test in a project through your coding agent. The model reads the code, plans the change, calls tools and returns a patch. Its Terminal-Bench 2.1 result in the card is 80.9. For newcomers and experts: This is a large model for building agents. It is not a quick download, so a good first step is to read the model card and decide whether your GPU can hold the 4-bit version. The card compares it with three rivals of similar size, Gemma4-31B, Qwen3.8-27B and Muse-Glimmer-30B, with the same inference settings for each. It leads on most agentic coding and cowork tables, but not on SWE-Bench Pro, GDPval or GPQA Diamond. ```flow You give the agent a task The model plans and thinks It calls tools and reads results It returns code or a finished answer ``` Good to know: These are the authors' own scores, and each benchmark has its own setup noted in the card. ```chart Title: Terminal-Bench 2.1 score Nanbeige4.3-33B: 80.9 Qwen3.8-27B: 73.0 Muse-Glimmer-30B: 61.8 Gemma4-31B: 47.2 ``` - Where it trails: SWE-Bench Pro 60.3 against 61.7 for Qwen3.8-27B, GDPval 84.8 against 86.4, and GPQA Diamond 89.5 against 91.3 for Gemma4-31B - Teacher role: the authors say it will feed a later distillation pipeline for smaller models such as Nanbeige4.3-3B - Other versions: 101 quantized and 11 fine-tuned versions made by other people are listed - License: Apache 2.0 - Cost to run: none named, you supply the GPU - Data it reaches: the prompts and any files or tools you connect to it ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 33.1B - Weights: about 66.2 GB in BF16; about 16.6 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 21,843 - Likes: 1,077 - License: apache-2.0 - Languages: en, zh - Based on: Nanbeige/Nanbeige4.3-33B-Base - Quantized versions: 102 - Fine-tunes: 12 - Tags: transformers, safetensors, llama, text-generation, nanbeige, conversational ## Links - Model: https://huggingface.co/Nanbeige/Nanbeige4.3-33B - Files: https://huggingface.co/Nanbeige/Nanbeige4.3-33B/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Nanbeige/Nanbeige4.3-33B ``` 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 [Nanbeige/Nanbeige4.3-33B on Hugging Face](https://huggingface.co/Nanbeige/Nanbeige4.3-33B) ## 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 | | --- | --- | --- | --- | --- | --- | | Nanbeige4.3-33B (this page) | 21,843 | 1,077 | 33.1B | apache-2.0 | 2026-10-28 | | [Nanbeige4.3-33B-INT4](https://mindket.com/p/6ce5c80d-8374-46fe-9a56-8640a8a3b951) | 749 | 10 | 33.1B | apache-2.0 | 2026-10-31 | | [Nanbeige4.2-3B by Nanbeige](https://mindket.com/p/4b18411b-9c39-46d8-88db-2a4a8ea71f5f) | 96,886 | 860 | 4.2B | apache-2.0 | 2026-09-11 | | [Nanbeige\_Nanbeige4.3-33B-GGUF](https://mindket.com/p/f4b76bb4-5c97-4ae2-b688-4922bacb571c) | 151,191 | 33 | 33.1B | apache-2.0 | 2026-10-25 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [TinyLlama-1.1B-Chat-v1.0](https://mindket.com/p/e73c1610-865e-48f4-a11d-4f6b640f3ae3) | 963,057 | 1,910 | 1.1B | apache-2.0 | 2024-03-17 | 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 · rene\_d · 2026-11-02](https://news.ycombinator.com/item?id=50336627): Nanbeige/Nanbeige4.3-33B - stronger coder than Qwen 3.8 - [Bluesky · AI Rush X · 2026-11-05](https://bsky.app/profile/airushx.bsky.social/post/3mzlc4vhafv22): 🧠 A free, open agent model you can run yourself. Nanbeige4.3-33B: what it claims, what it needs, what to test first. Full story 👉 https://airushx.com/nanbeige-4-3-33b-free-agent-model/ \#OpenSource \#AIAgents \#AI - [Hacker News · Retro\_Dev · 2026-10-29](https://news.ycombinator.com/item?id=50296877): Nanbeige4.3-33B – Flagship teacher LLM - [Bluesky · 🚀 olud.ai · 2026-11-02](https://bsky.app/profile/oludai.bsky.social/post/3mzck4ob4my24): 🤗 Climbing on Hugging Face right now: Nanbeige/Nanbeige4.3-33B ⬇ 13.8k downloads / 30 days · ♡ 932 · text generation · Apache-2.0 Open-weight models ranked by real-world usage, every day: https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace - [Hacker News · Philpax · 2026-10-25](https://news.ycombinator.com/item?id=50231099): Nanbeige/Nanbeige4.3-33B - [Bluesky · ♠️ Critical Nexus ✊🏿 · 2026-11-06](https://bsky.app/profile/criticalnexus.bsky.social/post/3mznjyrlhil2y): @ArtificialAnlys Nanbeige has released Nanbeige4.3-33B, an open weights model matching Qwen3.8 27B (xhigh) at 20 on the Artificial Analysis Intelligence Index v5 with stronger terminal use but weaker knowledge @Nanbeige is the foundation model res... https://x.com/i/web/status/2118400279788101824 - [Hacker News · rochansinha · 2026-10-26](https://news.ycombinator.com/item?id=50245941): Nanbiege 4.3-33B - [Bluesky · Raff Martin · 2026-11-03](https://bsky.app/profile/esatapedico.bsky.social/post/3mzekfnzbpc2z): Posted NVFP4 GGUFs for Gemma-4-31B, MiniCPM-V-4.7-35B-A3B, Nanbeige4.3-33B. Built for 32GB VRAM or less. If it's worth it on consumer cards, I'll make the family. Ping me with suggestions. https://www.esatapedico.com/posts/latest-model-uploads-gemma-minicpm-nanbeige/ \#ai \#inference \#open-source - [Bluesky · 🚀 olud.ai · 2026-11-01](https://bsky.app/profile/oludai.bsky.social/post/3mzblyz4rza2n): Nanbeige/Nanbeige4.3-33B: 13.8k downloads/30 days, 932 likes on HF. Open-weight Apache-2.0 text gen model climbing fast. What drives a 33B model’s traction? https://olud.ai/\#leaderboard \#OpenSource \#AI \#HuggingFace - [Bluesky · SunLibCrypto · 2026-11-09](https://bsky.app/profile/sunlibcrypto.bsky.social/post/3mzvffbrssx2q): 🆕 Nouvel article SunLibCrypto — Nanbeige4.3-33B égale Qwen3.8-27B sur l'index d'IA mais consomme 44 % de jetons en plus Le modèle de raisonnement dense Nanbeige4.3-33B de BOSS Zhipin obtient https://sunlibcrypto.com/promo-clic.php?id=e0dce5b191723721bede6b43e17879a82bfc4dcf23cc2a3d10cdfd3f41edad13 - [Bluesky · Blade.sh · 2026-11-05](https://bsky.app/profile/blade-sh.bsky.social/post/3mzldqgiv4r2d): New on Blade.sh \#1: 25 new open-weight models. Two to try first: - Gemma 4 31B (@GoogleDeepMind) reads screenshots, charts and docs, for support and ops teams - Nanbeige4.3-33B (@nanbeige) automates multi-step tasks, for engineering teams Same OpenAI-compatible API. - [Bluesky · デイリーHuggingFaceトレンド · 2026-11-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mzbatmmkuj2s): 今日のHuggingFaceトレンド Nanbeige/Nanbeige4.3-33B 軽量エージェントの開発を目的としています。 まず30Bパラメータ規模で高い性能を持つ教師モデルNanbeige4.3-33Bを構築し、複雑なコーディングや専門的な共同作業タスクにおける能力を向上させました。 この教師モデルを基に、将来的にさらに小規模なモデルへ高度なエージェント能力を蒸留するための基盤を提供します。 ## 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: 2026-10-28 - First published: 2026-10-24 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Nanbeige/Nanbeige4.3-33B on Hugging Face](https://huggingface.co/Nanbeige/Nanbeige4.3-33B) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c7975e8b-eb6f-497a-a398-2028206aa2a0) - [Its page on Mindket](https://mindket.com/p/c7975e8b-eb6f-497a-a398-2028206aa2a0)