Nanbeige4.3-33B: BOSS Zhipin lab's open-weights agentic coding model

Nanbeige, the large language model lab at BOSS Zhipin, has published Nanbeige4.3-33B on Hugging Face as open weights, with a context of up to 512K tokens. The repository was created October 24, 2026. Nanbeige says the model leads its size class on agentic coding; on the lab's own tests, which also produced every rival's score, it has the best result on 17 of 20 benchmarks and trails on SWE-Bench Pro, GDPval and GPQA Diamond.

#What changed?

Nanbeige calls the model "the flagship teacher model for the Nanbeige4.3 series." The lab says it delivers "state-of-the-art agentic performance among open models in the ~30B parameter class" and "excels at complex coding and professional cowork tasks." It is fine-tuned from Nanbeige4.3-33B-Base, published at the same time. Earlier Nanbeige models on Hugging Face were 3B-class, such as Nanbeige4.2-3B.

Key details from the model card and repository:

The card does not say how the 512K context is achieved, how many tokens the model was trained on, or whether it is dense or mixture-of-experts. The card has no limitations or safety section. The lab says smaller distilled models will follow, giving Nanbeige4.3-3B as an example, with no date.

#What do Nanbeige's benchmarks show?

Nanbeige compares the model with Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B. Muse-Glimmer-30B is a comparison model from Meta Superintelligence Lab, not part of Nanbeige's lineage. "All models are evaluated at their highest thinking effort," the card says, and "For each benchmark, we use the same inference hyperparameters and harness across models." So every number below, rivals included, is Nanbeige's own run, taken from the card's table.

The bold figure is the best in each row, per Nanbeige's table. The lab says "Terminal-Bench 2.1 is evaluated using Terminus 2" and "Terminal-Bench 4 and DeepSWE are evaluated using mini-swe-agent v2.4.6." By these tests, Nanbeige4.3-33B trails Qwen3.8-27B on SWE-Bench Pro (60.3 vs 61.7) and GDPval (84.8 vs 86.4), and trails Gemma4-31B on GPQA Diamond (89.5 vs 91.3). YC-bench reports a dollar score.

No independent leaderboard lists the model. Artificial Analysis showed only Nanbeige4.1-3B on its Nanbeige page, and no Nanbeige submission was found on the Terminal-Bench 2.1 leaderboard, as of October 25, 2026.

#Who can use it and when?

The weights are downloadable from Hugging Face without an access request. The card and Hugging Face list the model as Apache 2.0, but the repository has no LICENSE file, so the license rests on that listing. Nanbeige publishes no price, and the card names no hosted API.

Nanbeige has not released an official quantized build of Nanbeige4.3-33B. Community-made GGUF builds from at least eight third-party accounts appeared on Hugging Face on October 25, 2026, the first at 12:42 UTC (18:12 IST). They are not from Nanbeige.

Startup Fortune, an independent outlet that covered the release, claims that "A 4-bit quantisation can run on a single 24GB consumer or prosumer card rather than a rack of H100s." It also says the release "landed on r/LocalLLaMA and quickly climbed past 140 points." Both are Startup Fortune's claims; Nanbeige says nothing about quantization or consumer GPUs. Startup Fortune adds that "Nobody should read this as a clean sweep."

#How to get it

Nanbeige's card says: "You can run the model with Transformers, SGLang, or vLLM:"

• Set the model path, per the card: MODEL_PATH=/path/to/your/Nanbeige4.3-33B
• SGLang, written on one line: python -m sglang.launch_server --model-path ${MODEL_PATH} --host 0.0.0.0 --port 8000 --tp-size 4 --mem-fraction-static 0.8 --reasoning-parser qwen3 --tool-call-parser qwen3_coder
• vLLM, written on one line: vllm serve ${MODEL_PATH} --host 0.0.0.0 --port 8000 --tensor-parallel-size 4 --gpu-memory-utilization 0.8 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder
• Transformers: the card's snippet loads model_id = 'Nanbeige/Nanbeige4.3-33B' with trust_remote_code=True.

Both server commands use four-way tensor parallelism. Thinking mode "is enabled by default." For settings, the card recommends:

1. Tool calling. "We recommend tool_call_format="xml" for the best tool-calling performance; json is also supported for compatibility."
2. preserve_thinking. "We recommend False for general chat and question answering, and True for multi-turn tool use, office tasks, and code-agent workflows."
3. Sampling. Temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agentic and tool-use tasks; temperature 0.6, top_p 0.95 and up to 262,144 new tokens for reasoning and chat.

#How does it compare?

The closest recent release we covered is Recursive Flint, another open-weight coding model with Terminal-Bench scores. Like Hugging Face's Carbon-X, Nanbeige4.3-33B is listed as Apache 2.0 on Hugging Face. For another Chinese lab's recent model update, see DeepSeek V4.1 Flash 1017. Muse-Glimmer-30B, one of Nanbeige's baselines, comes from Meta's Muse line, which we covered with the Muse Wonder launch.

#What is still unclear?

• Independent scores. Every number, including the rivals', is Nanbeige's own.
• License text. The repository has no LICENSE file.
• Training details. The card does not give training data, token count or how 512K context is reached.
• Hardware. Nanbeige states no hardware requirements beyond its four-GPU example commands.
• Release time. Hugging Face shows when the repository was created, not when it became public, and the lab posted no announcement.