Nanbeige4.3-33B: Open agent model with 512K context beats Qwen3.8-27B on most tests
The Nanbeige team has released Nanbeige4.3-33B on Hugging Face. It's built for coding and office-agent tasks, and by the team's own benchmarks it beats Qwen3.8-27B and Gemma4-31B on many tests. It also serves as a teacher model for smaller variants, such as an upcoming 3B model.
What's going on
The Nanbeige team has released the language model Nanbeige4.3-33B on Hugging Face. It's the flagship of the Nanbeige4.3 series and is meant mainly as a "teacher": first the team builds a powerful agent model, then transfers its abilities to smaller models through distillation. Distillation means a large model teaches a small model to respond in a similar way, so the small model can do more than its size would suggest. The team names a Nanbeige4.3-3B as its next target.
The developer describes the model as the leader among open models in the roughly 30-billion-parameter class (parameters are, roughly speaking, the knobs the model tuned during training). It is said to be especially strong at complex programming tasks and "professional cowork tasks", meaning office and automation work that an agent handles on its own. The sources are the model page on Hugging Face and a post on r/LocalLLaMA.
Benchmark comparison
The comparison covers Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B. According to the developer, all models were tested at the highest reasoning effort, with the same inference settings and the same test environment ("harness"). These are the developer's own figures, not independent measurements.
| Benchmark | Nanbeige4.3-33B | Qwen3.8-27B | Muse-Glimmer-30B | Gemma4-31B |
|---|---|---|---|---|
| Terminal-Bench 2.1 | 80.9 | 73.0 | 61.8 | 47.2 |
| Terminal-Bench 4.0 | 23.8 | 6.3 | 1.6 | 0.0 |
| DeepSWE | 65.5 | 42.5 | 4.4 | 2.7 |
| SWE-Bench Pro | 60.3 | 61.7 | 36.9 | 51.2 |
| Automation Bench | 54.2 | 38.3 | 15.2 | 9.7 |
| JobBench | 44.8 | 34.5 | 16.6 | 9.0 |
| GDPval | 84.8 | 86.4 | 75.8 | 71.4 |
| Toolathlon-Verified | 70.4 | 68.5 | 47.2 | 27.8 |
| GPQA Diamond | 89.5 | 89.2 | 83.5 | 91.3 |
| AIME 2026 | 95.2 | 94.5 | 94.7 | 89.2 |
Other results: SWE-Atlas-QnA 50.8, NL2Repo 53.0, Scicode 52.6, SkillsBench 61.1, Claw-Eval 78.3, DeepPlanning 63.3, EnterpriseOps-Gym 41.0, Tau3-Bench 48.8 and AA-LCR 84.0. On the YC-bench simulation, it earns US$1,025,437 versus $641,290 for Qwen3.8-27B. On JobBench, the developer says it beats the comparison models in most of the 35 professions tested.
The footnotes show that some test conditions are unusual: Nanbeige was tested with preserve_thinking=true, SWE-Atlas-QnA was graded by GPT-6 Sol as the judge, GDPval used revised grading rubrics, and Toolathlon-Verified had no per-request token limit.
Thinking mode, tools and context
The chat-template tokenizer offers several switches:
enable_thinking: controls whether the model "thinks" before answering. On by default; set it toFalsefor non-thinking mode.preserve_thinking: keeps the reasoning from earlier turns of the conversation. Recommended:Falsefor normal chat,Truefor multi-step tool use, office tasks and coding agents.- Tools are passed in through the template. For best results, the team recommends
tool_call_format="xml"; JSON works too.
Context extends to 524,288 tokens (tokens are chunks of text, roughly word fragments). Recommended sampling settings:
| Scenario | Temperature | Top_p | Max new tokens |
|---|---|---|---|
| Agents and tools | 1.0 | 0.95 | 65,536 |
| Reasoning and chat | 0.6 | 0.95 | 262,144 |
What you need to try it
A 33-billion-parameter model needs roughly 66 GB of memory at full 16-bit precision, and the example commands for vLLM and SGLang use four GPUs. The source says nothing about quantized versions (GGUF), so it's unclear whether or when they'll appear. At 4-bit, expect roughly 20–22 GB of VRAM, so a 24 GB card or a Mac with plenty of unified memory. The longer the context, the more memory the cache eats. Also plan for about 70 GB of disk space for the original weights. Google Colab and Kaggle are linked on the page, but the free tiers are barely enough for a 33B model.
You can run it with Transformers (with trust_remote_code=True), vLLM or SGLang; both servers offer an OpenAI-compatible API. For tool calls, use the parsers qwen3 (reasoning) and qwen3_coder (tools).
Our take
The model is interesting if you want to run agents for programming and office automation locally or self-hosted. The numbers are impressive, especially on Terminal-Bench 4.0 and DeepSWE, where Nanbeige is far ahead of the others. At the same time, these are the developer's own figures, partly with custom settings, and a competitor leads on SWE-Bench Pro, GDPval and GPQA Diamond. So "stronger than Qwen3.8-27B" holds for most tests, not all of them. Independent verification, the license and community quantizations are still to come. It will be interesting to see how good the announced small distilled models, like the 3B, turn out to be.
Sources
- r/LocalLLaMA huggingface.co
- r/singularity reddit.com
- Google News (US) news.google.com
- Google News (DE) news.google.com
- Google News (FR) news.google.com
- Google News (ES) news.google.com
- Google News (BR) news.google.com
- Google News (DE) news.google.com
- Google News (US) news.google.com
- Bing News (en-GB) aiweekly.co
- r/LocalLLaMA huggingface.co
- MarkTechPost marktechpost.com
Try it yourself: Nanbeige4.3-33B
Explained step by step, no prior knowledge needed – on your own computer or free in your browser.
What can I do with it?
The model works as a reasoning and agent model: it can solve programming tasks in the terminal, edit code repositories, call tools and work through office workflows. It handles very long inputs of up to 512K tokens and can tackle tricky math and logic questions in thinking mode.
Who is it worth it for?
Worth it for developers and tinkerers with a strong GPU or multi-GPU setup who want to test local coding agents. Not really for beginners with a small graphics card, at least until smaller variants or quantizations are available.
What your computer needs
According to the source, only the original 33B weights exist so far. For 4-bit use (once community quantizations appear), about 22 GB of VRAM is realistic; for full precision you need several GPUs. A smaller Nanbeige4.3-3B has been announced but isn't out yet.
- The example commands for vLLM and SGLang use --tensor-parallel-size 4, which means four GPUs.
- At 16-bit, the model needs roughly 66 GB for the weights; at 4-bit, roughly 20–22 GB.
- Long contexts (up to 512K) cost a lot of extra memory; start with shorter contexts.
- The source doesn't mention a GGUF version for CPU or Apple Silicon use.
How do I find out what my computer has?
- Windows: Press Ctrl + Shift + Esc → Task Manager → Performance tab → click GPU on the left. At the bottom you'll see Dedicated GPU memory (that's your VRAM). You'll find system memory under Memory.
- Mac: Apple menu at the top left → About This Mac. You'll see your chip (e.g. M2) and memory (e.g. 16 GB). On Apple chips, graphics and processor share this memory.
- Linux: Type
nvidia-smiin the terminal (shows your graphics card and its memory) andfree -hfor system memory.
Guide
Google Colab (in your browser, no installation)
Google Colab is a free service from Google. It gives you a computer with a graphics card in your browser for a few hours. All you need is a Google account. For this model: Colab Pro only – needs an A100 with 40 GB.
- Download the notebook. Click the button and save the file.
- Open Colab. Go to colab.research.google.com and sign in with your Google account.
- Upload the notebook. In the window that appears, click Upload on the left (or choose File → Upload notebook at the top) and select the file
colab.ipynb. - Turn on the GPU. In the top menu, go to Runtime → Change runtime type → under Hardware accelerator, select T4 GPU (with Colab Pro: L4 or A100) → Save.
- Start everything. Runtime → Run all (keyboard shortcut Ctrl + F9). If you see the warning “This notebook was not authored by Google,” click Run anyway.
- Wait. The cells run one after another. A green checkmark appears to the left of each cell when it's done. Installation and download take about 10 minutes here.
- Try it out. The result appears below the last cell. To test something else, change the text in the cell and run it again with Shift + Enter. The last cell starts a proper chat with an input field.
- Clean up. When you're done: Runtime → Disconnect and delete runtime. This saves your free quota.
Good to know: The free GPU is limited (usually a few hours per day). After about 90 minutes of inactivity, Colab disconnects, and you'll have to start everything over.
Windows 10 / 11
The easiest way is Ollama, a free program that launches AI models with a single command.
- Update your graphics driver (NVIDIA cards only): Install the NVIDIA app or the latest driver. For AMD cards, use the latest Adrenalin driver.
- Install Ollama. Download the file OllamaSetup.exe from ollama.com/download/windows, double-click it, and choose Install. Afterward, a small llama icon appears in the taskbar at the bottom right.
- Open a terminal window. Press the Windows key, type PowerShell and press Enter. A blue or black window opens.
- Start the model. Copy this command ("Copy" button), paste it into the window with a right-click, and press Enter:
The first time, Ollama downloads the model (19 GB (about 25 min at 100 Mbit/s)). After that, it's instant.ollama run hf.co/eepos/Nanbeige4.3-33B-Q4_K_S-GGUF:Q4_K_S - Start typing. Once
>>> Send a messageappears, type your question and press Enter. Type/byeto end the chat.
Prefer automatic? A script that picks the right variant for you
- Download both files into the same folder: try_windows.ps1 demo.py
- Open the folder in File Explorer, right-click an empty spot and choose Open in Terminal.
- Paste this command and press Enter:
powershell -ExecutionPolicy Bypass -File try_windows.ps1
If needed, the script installs Ollama via winget, measures your VRAM (video memory) and downloads the matching variant.
macOS
Macs with an Apple chip (M1 to M5) are ideal. Older Intel Macs are very slow.
- Install Ollama. Download it from ollama.com/download/mac, open the file, and drag Ollama into your Applications folder. Launch Ollama once – a llama appears in the menu bar at the top.
- Open Terminal. Press Cmd + Space, type Terminal, and press Enter.
- Start the model. Paste the command (Cmd + V) and press Enter:
The first time, the model is downloaded (19 GB (about 25 min at 100 Mbit/s)).ollama run hf.co/eepos/Nanbeige4.3-33B-Q4_K_S-GGUF:Q4_K_S - Start typing. Type your question and press Enter. Quit with
/bye.
Linux
- Check your GPU.
nvidia-smishould show your card. If it doesn't, install the driver on Ubuntu and restart:sudo ubuntu-drivers install - Install Ollama:
curl -fsSL https://ollama.com/install.sh | sh - Start the model:
Exit withollama run hf.co/eepos/Nanbeige4.3-33B-Q4_K_S-GGUF:Q4_K_S/bye. Useollama psto check whether the model is running on the GPU. - Or let it pick the right variant automatically:
bash try_linux.sh # mit Python/transformers statt Ollama: MODE=hf bash try_linux.sh
Try it
Write a Python script that reads all CSV files in a folder, merges them and removes duplicates. Then briefly explain how it's structured.Which number is larger, 9.11 or 9.8? Explain step by step.Plan a five-day project week for a team of four, with tasks, dependencies and risks in a table.If something goes wrong
When loading with Transformers, set `trust_remote_code=True` and `use_fast=False`, as shown in the source.
For tool calls, pass the parsers `--reasoning-parser qwen3` and `--tool-call-parser qwen3_coder`.
For agent tasks, use `preserve_thinking=True` and temperature 1.0; for normal chat, use `False` and temperature 0.6.
The source doesn't name the license; check the model page before any commercial use.
“out of memory,” a crash, or your computer freezes
The model is too big for your VRAM (video memory). Pick a smaller variant from the table above and close other programs, especially games and browsers with lots of tabs.
It runs, but extremely slowly
Your CPU is probably doing the work instead of your graphics card. In Ollama, the PROCESSOR column of ollama ps shows how much runs on the GPU. If it says "100% CPU", update your graphics driver or pick a smaller variant that fits entirely into VRAM (video memory).
“ollama” or “python” is not found
Close the terminal window and open it again so it recognizes the newly installed program. For Python on Windows: reinstall it and check Add python.exe to PATH.
Windows: "Running scripts is disabled on this system"
Run the script with exactly the command above: powershell -ExecutionPolicy Bypass -File try_windows.ps1. This allows execution for this one time only.
Error message when loading the model (version, "trust_remote_code", unknown architecture)
Brand-new models often need the latest software version. With Ollama: update Ollama. With Python: pip install -U transformers in the activated environment. The Model Card usually lists the exact version.
Tokenstrom generates the scripts and notebook automatically. Please take a quick look before running them – they're plain, readable text. Mind the model's license: apache-2.0.
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