Nanbeige LLM Lab根据Apache 2.0许可证在Hugging Face上拥有发布 Nanbeige4.3-33B. Nanbeige4.3-33B是一种33B的密集语言模型，用于代理编码和办公室式的"合作"任务。 实验室坐落于招募平台BOSS Zhipin内。 它还出版了"南碧格4.3-33B-Base"，用于定制后培训。 该模型支持524,288个托肯上下文窗口。 在实验室自己的桌子上，它引领了20基准测试中的17个，对准30B附近的3个开放型号。
TL；数据
- 大小：33B参数，密度(无MOE),524,288-token(512K)上下文，BF16重量。
多GPU服务器。 卡片的SGLang和vLLM配方使用4路的抗拉强度平行法。 BF16重量需要约66GB(我们的算术). 列出1个社区量化。
- 性能：在南碧芝自己与Qwen3.8-27B,Muse-Glimer-30B和Gemma4-31B的对比中领先20列中的17列。
- 最佳战绩：80.9客场-奔驰2.1客场对阵73.0客场Qwen3.8-27B客场。
- 最差：23.8分，终点站-奔驰4.0(其最低分). 它还在SWE-Bench Pro,60.3 vs 61.7.
南碧盖4.3-33B是什么？
Nanbeige4.3-33B是Nanbeige4.3系列的旗舰 " 教师 " 模式。 实验室的既定策略是先培养一个强壮的教师。 随后它计划将代理技能提炼成更小的模型，从南北革4.3-3B开始。
南碧盖4.3-33B如何建成？.
config.json宣布标准LlamaForCausalLM架构。 没有专家的路由，所以所有33B参数都运行在每个符号上。
- 52层，隐藏大小7 168
- 56个查询头和8个关键值头(分组-清点)，头尺寸128
- FFN中间尺寸22 016，词汇166 912个符号
- 120 000 000和524 288个最高职位
聊天模板曝光3个控件。 启用 thinking 切换推理并默认进行。 保存思考让推理从早期的转折。 Nanbeige推荐它用于多回合工具使用和代码agents. 通过工具可以使工具调用，建议采用XML调用格式。
南碧盖4.3-33B在基准测试上表现如何？.
南碧姬将该型号与Qwen3.8-27B,Muse-Glimer-30B和Gemma4-31B进行了比较。 每个模型都以最高的思维力运行，拥有共享的超参数和杠杆。 本节各分自报。
- 代理编码：在Bench 2.1号终端上得分80.9分，Qwen3.8-27B则得分73.0分。 DeepSWE 显示最宽差距：65.5对42.5分。 在Terminal-Bench 4.0上，它得分23.8，而Gemma4-31B则得分0.0.
- 代理合作：在自动化座椅上公布54.2个，在乔布本奇上公布38.3个，在乔布本奇上公布44.8个，在34.5个。 在YC-bench上，模拟收入达到1 025 437美元，对641 290美元。
- 理由：它报告了95.2项关于AIME 2026,84.0项关于AA-LCR。
其输二十行中三。 Qwen3.8-27B领先SWE-Bench Pro(61.7对60.3)和GDPval(86.4对84.8). Gemma4-31B领先GPQA Diamond在南碧杰的桌子，91.3对89.5.
基地模型在所有8项报告任务上均击败Gemma4-31B-Base. 例如MMLU-Pro(66.9诉59.3)和GPQA(62.0诉41.5)。
它如何与竞争对手自己的样板卡相比较？
* 南碧杰自报。 竞争者的价值来自每个实验室自己的Hugging Face型号卡。
有些竞争者在南碧姬的表格中
- Meta报告Muse-Glimer-30B在SWE-Bench Pro的51.2上，Gemma4-31B在36.9上。 南碧盖列出反。
- Google在GPQA Diamond上列出Gemma 4 31B 84.3位。 南碧盖的桌子显示 91.3.
- Meta报告Muse-Glimer-30B在Bench 2.1号航站楼51.7号。 南碧芝测得61.8分。
不同的绳索可以解释一些差距。 SWE -Bench Pro的对看起来是互换的。 在独立运行出现之前将这些行作为临时的。
如何部署南北4.3-33B?.
模型卡提供变形金刚，SGLang和vLLM配方。 它的服务器示例使用与 Qwen3 推理和工具调用解析器的4-way lator并行化。 推荐的 agent 设置为温度 1.0，上 p 0.95 和 65,536 最大新令牌。 理性和聊天使用气温0.6和262,144 最大新符号。
通过config.json的算术，BF16重量需要约66GB. KV缓存的新增在BF16中每令牌约208KiB. 因此，完整的524,288个托肯序列大约需要112GB的KV缓存。 在出版时，没有Hugging Face推论提供者主持模型。
关键外卖
- 33B 稠密，Apache 2.0, 512K 上下文，仅限文本。
- 在Bench 2.1号终端站和DeepSWE站站点自报80.9号终端站和65.5号终端站。
- 南比吉自己的桌子上排了20行中的17行。
- 几位竞争者人物与公务卡冲突。
- 建为即将来临的南碧格4.3-3B的老师。
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Nanbeige LLM Lab has released Nanbeige4.3-33B on Hugging Face under the Apache 2.0 license. Nanbeige4.3-33B is a 33B dense language model built for agentic coding and office-style ‘cowork’ tasks. The lab sits inside the recruitment platform BOSS Zhipin. It also published Nanbeige4.3-33B-Base for custom post-training. The model supports a 524,288-token context window. In the lab’s own table, it leads 17 of 20 benchmarks against 3 open models near 30B.
TL;DR
- Size: 33B parameters, dense (no MoE), 524,288-token (512K) context, BF16 weights.
- Runs on: Multi-GPU servers. The card’s SGLang and vLLM recipes use 4-way tensor parallelism. BF16 weights need about 66 GB (our arithmetic). 1 community quantization is listed.
- Performance: Leads 17 of 20 rows in Nanbeige’s own comparison against Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B.
- Best: 80.9 on Terminal-Bench 2.1, versus 73.0 for Qwen3.8-27B.
- Worst: 23.8 on Terminal-Bench 4.0 (its lowest score). It also trails Qwen3.8-27B on SWE-Bench Pro, 60.3 vs 61.7.
What is Nanbeige4.3-33B?
Nanbeige4.3-33B is the flagship ‘teacher’ model of the Nanbeige4.3 series. The lab’s stated strategy is to build a strong teacher first. It then plans to distill agentic skills into smaller models, starting with Nanbeige4.3-3B.
How is Nanbeige4.3-33B built?
The config.json declares a standard LlamaForCausalLM architecture. There is no expert routing, so all 33B parameters run on every token.
- 52 layers, hidden size 7,168
- 56 query heads and 8 key-value heads (grouped-query attention), head dimension 128
- FFN intermediate size 22,016, vocabulary of 166,912 tokens
- RoPE theta of 120,000,000 and 524,288 max positions
The chat template exposes 3 controls. enable_thinking toggles reasoning and is on by default. preserve_thinking keeps reasoning from earlier turns. Nanbeige recommends it for multi-turn tool use and code agents. Passing tools enables tool calling, and an XML call format is recommended.
How does Nanbeige4.3-33B perform on benchmarks?
Nanbeige compared the model with Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B. Every model ran at its highest thinking effort, with shared hyperparameters and harnesses. All scores in this section are self-reported.
- Agentic coding: It scores 80.9 on Terminal-Bench 2.1, against 73.0 for Qwen3.8-27B. DeepSWE shows the widest gap: 65.5 versus 42.5. On Terminal-Bench 4.0, it scores 23.8 while Gemma4-31B scores 0.0.
- Agentic cowork: It posts 54.2 on Automation Bench versus 38.3, and 44.8 on JobBench versus 34.5. On YC-bench, simulated earnings reach $1,025,437 versus $641,290.
- Reasoning: It reports 95.2 on AIME 2026 and 84.0 on AA-LCR.
It loses 3 of 20 rows. Qwen3.8-27B leads SWE-Bench Pro (61.7 vs 60.3) and GDPval (86.4 vs 84.8). Gemma4-31B leads GPQA Diamond in Nanbeige’s table, 91.3 vs 89.5.
The Base model beats Gemma4-31B-Base on all 8 reported tasks. Examples include MMLU-Pro (66.9 vs 59.3) and GPQA (62.0 vs 41.5).
How does it compare with competitors’ own model cards?
*Self-reported by Nanbeige. Competitor values come from each lab’s own Hugging Face model card.
Some competitor figures in Nanbeige’s table differ from what those labs publish:
- Meta reports Muse-Glimmer-30B at 51.2 on SWE-Bench Pro and Gemma4-31B at 36.9. Nanbeige lists the reverse.
- Google lists Gemma 4 31B at 84.3 on GPQA Diamond. Nanbeige’s table shows 91.3.
- Meta reports Muse-Glimmer-30B at 51.7 on Terminal-Bench 2.1. Nanbeige measured 61.8.
Different harnesses explain some gaps. The SWE-Bench Pro pair looks swapped. Treat these rows as provisional until independent runs appear.
How do you deploy Nanbeige4.3-33B?
The model card provides Transformers, SGLang and vLLM recipes. Its server examples use 4-way tensor parallelism with Qwen3 reasoning and tool-call parsers. Recommended agent settings are temperature 1.0, top_p 0.95 and 65,536 max new tokens. Reasoning and chat use temperature 0.6 and 262,144 max new tokens.
By arithmetic from config.json, BF16 weights need about 66 GB. The KV cache adds about 208 KiB per token in BF16. A full 524,288-token sequence therefore needs roughly 112 GB of KV cache. At publication, no Hugging Face Inference Provider hosts the model.
Key Takeaways
- 33B dense, Apache 2.0, 512K context, text-only.
- Self-reported 80.9 on Terminal-Bench 2.1 and 65.5 on DeepSWE.
- Leads 17 of 20 rows in Nanbeige’s own table.
- Several competitor figures conflict with official cards.
- Built as the teacher for the upcoming Nanbeige4.3-3B.
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