Nanbeige 发布 33B 开源智能体编程模型 Nanbeige4.3-33B

Nanbeige Releases Nanbeige4.3-33B, a 33B Open Agentic Coding Model

Nanbeige LLM Lab（BOSS 直聘旗下）在 Hugging Face 以 Apache 2.0 协议发布 33B 稠密模型 Nanbeige4.3-33B，主打智能体编程与办公协作任务，支持 524,288-token 上下文，同时开放 Base 版供自定义后训练。

另外 1 家也报道了正文 · 原文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.

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.

The config.json declares a standard LlamaForCausalLM architecture. There is no expert routing, so all 33B parameters run on every token.

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.

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.

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).

*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:

Different harnesses explain some gaps. The SWE-Bench Pro pair looks swapped. Treat these rows as provisional until independent runs appear.

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.

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