Nanbeige4.3-33B is a free, open-weight coding and agent model from Nanbeige LLM Lab, released on Hugging Face on October 25, 2026. It carries an Apache 2.0 license, a 512K-token context window, and reasoning switched on by default.
The lab says it beats Qwen3.8-27B on most of its own tests. That’s a claim, not a verdict, because nobody outside the lab has reproduced the numbers yet.
Here are the basics at a glance:
- Released: October 25, 2026, on Hugging Face
- Size: 33 billion parameters, BF16 weights
- Context window: up to 524,288 tokens (512K)
- License: Apache 2.0, so commercial use is allowed
- Benchmarks: self-reported, not independently verified
Those five facts decide whether this model is worth your weekend. The rest of this post covers what’s behind them.
What Nanbeige4.3-33B actually is
Nanbeige calls it the flagship “teacher” model of the Nanbeige4.3 series. The idea is to use the big model to train smaller ones, such as a planned Nanbeige4.3-3B.
Why does that matter to you? Smaller models distilled from a strong teacher often run on ordinary hardware, so this release may be a preview of lighter options coming later. Nothing is confirmed on timing.
It’s built for agentic coding, meaning it can call tools and work through multi-step tasks in a terminal. Thinking mode is on by default, and you can switch it off for quick replies.
Open weights are getting crowded. Moonshot has promised weights for its model too, as we covered in our Kimi K3.1 Pro breakdown. The difference here is that Nanbeige’s weights are already downloadable.
How the benchmark claims stack up against Qwen3.8-27B
According to the official model card, Nanbeige4.3-33B leads Qwen3.8-27B on 18 of the 20 tests it lists. Here are five of them, side by side:
| Benchmark | Nanbeige4.3-33B | Qwen3.8-27B | Who leads |
|---|---|---|---|
| Terminal-Bench 2.1 | 80.9 | 73.0 | Nanbeige |
| DeepSWE | 65.5 | 42.5 | Nanbeige |
| AIME 2026 | 95.2 | 94.5 | Nanbeige (narrowly) |
| SWE-Bench Pro | 60.3 | 61.7 | Qwen |
| GDPval | 84.8 | 86.4 | Qwen |
The big gap on DeepSWE stands out, and so does the near tie on math. Qwen still wins two tests, including SWE-Bench Pro, which is closest to real bug-fixing work.
Treat all of it with care. These are the lab’s own scores, and some use custom test setups, so comparisons are approximate. Independent testing usually narrows gaps like these.
Can you run Nanbeige4.3-33B on your own PC?
Probably, but not at full precision. At BF16, the weights alone take roughly 66GB before you add memory for the context. The model card doesn’t list a VRAM requirement, and its serving examples use four GPUs.
A 4-bit version should shrink the weights to around 20GB. That’s my own estimate from the parameter count, not a published figure. Community GGUF builds, like this one on Hugging Face, have already appeared, though they aren’t from Nanbeige. The card also points to llama.cpp, Ollama, and LM Studio for quantized builds.
If you’ve been looking at big local models, our look at Recursive Flint, a 40B coding model for a 48GB Mac is a useful comparison point.
What the 512K context window means for you
A 512K window is roughly enough to hold a mid-sized codebase, plus your instructions, in a single session. That’s the main appeal for coding agents, which need to read many files before they change one.
The catch is memory. Long contexts need extra RAM or VRAM on top of the model weights, so a laptop that loads the model may still struggle once you fill the window. Start with shorter sessions and work upward.
Another limit: the model card says no hosted inference provider serves it right now. If you want to try it today, you’ll be running it yourself, through Transformers, vLLM, SGLang, or a quantized build.
Settings that matter when you try it
Nanbeige publishes two recommended setups. Using the wrong one is an easy way to think the model is worse than it is.
- Agent and tool tasks: temperature 1.0, top_p 0.95, up to 65,536 new tokens.
- Chat and reasoning: temperature 0.6, top_p 0.95, up to 262,144 new tokens.
One more switch is preserve_thinking. The card suggests keeping it off for casual chat and turning it on for tool use and code agents, so earlier reasoning carries across turns.
Frequently Asked Questions
Is Nanbeige4.3-33B really open source?
The weights are open under Apache 2.0, which lets you use and modify them commercially. That’s usually called open-weight rather than fully open source, since training data and code aren’t part of the release.
Is it better than Qwen3.8-27B?
On Nanbeige’s own tests, mostly yes. Qwen3.8-27B still leads on SWE-Bench Pro and GDPval, and no outside group has confirmed the results.
How much memory does it need?
About 66GB for the BF16 weights alone. A 4-bit build should need roughly 20GB, by my estimate, plus extra for long contexts.
Can I use it for commercial projects?
Apache 2.0 generally allows it. Read the license text on the model page before you ship anything, since that’s the document that counts.
Worth a test, not a switch yet
If you already run local models, download a 4-bit build and point it at a few of your own coding tasks. Your repo will tell you more than any leaderboard. If you rely on a hosted model for real work, wait a week or two for independent results before you move anything.
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