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Nanbeige 发布 Nanbeige4.3-33B 教师模型,主打约 30B 级开源智能体性能

原标题:Nanbeige/Nanbeige4.3-33B (text-generation)

Nanbeige 发布 Nanbeige4.3-33B,定位为 Nanbeige4.3 系列的旗舰教师模型,自称在约 30B 参数开源模型中智能体性能领先,并作为后续向 Nanbeige4.3-3B 等小模型蒸馏智能体能力的基础。模型基于 Nanbeige4.3-33B-Base 微调,Apache-2.0 许可,支持中英文。官方基准显示其在 Terminal-Bench 2.1 得 80.9、DeepSWE 得 65.5、AIME 2026 得 95.2,多数基准领先 Qwen3.8-27B、Muse-Glimmer-30B 和 Gemma4-31B,但 SWE-Bench Pro 和 GDPval 落后于 Qwen3.8-27B;JobBench 覆盖 35 个职业中的大多数项目优于基线。

为什么值得看

官方给出与同量级开源模型的多基准对比数据和评测设置,读者可据此评估该 33B 模型在智能体任务上的表现。

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原文

Hugging Face 热门模型第 6 名:Nanbeige/Nanbeige4.3-33B,362 赞,近期下载 0 次。 标签:transformers, safetensors, llama, text-generation, llm, nanbeige, conversational, en, zh, base_model:Nanbeige/Nanbeige4.3-33B-Base, base_model:finetune:Nanbeige/Nanbeige4.3-33B-Base, license:apache-2.0

README:

1. Introduction

In Nanbeige4.3, we take a foundational step toward pushing the boundaries of lightweight agents. Our strategy centers on building a high-capacity teacher agent first, which will subsequently anchor the distillation of advanced agentic capabilities into smaller models.

To this end, we first introduce Nanbeige4.3-33B, designed as the flagship teacher model for the Nanbeige4.3 series. Delivering state-of-the-art agentic performance among open models in the ~30B parameter class, Nanbeige4.3-33B excels at complex coding and professional cowork tasks. It not only offers a powerful, cost-effective option for immediate real-world deployment, but also lays the necessary foundation to power our upcoming small model distillation pipeline (e.g., for Nanbeige4.3-3B).

2. Model Performance

We compare Nanbeige4.3-33B with recently released frontier models at around the 30B-parameter scale: Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B. All models are evaluated at their highest thinking effort. For each benchmark, we use the same inference hyperparameters and harness across models.

  CategoryBenchmarkNanbeige4.3-33B1Qwen3.8-27BMuse-Glimmer-30BGemma4-31B



Agentic CodingTerminal-Bench 2.1280.973.061.847.2
Terminal-Bench 4.0323.86.31.60.0
DeepSWE365.542.54.42.7
SWE-Bench Pro360.361.736.951.2
SWE-Atlas-QnA450.841.917.73.3
NL2Repo553.048.327.433.8
Scicode52.646.744.945.5
SkillsBench61.156.439.728.9
Agentic CoworkAutomation Bench654.238.315.29.7
JobBench744.834.516.69.0
GDPval884.886.475.871.4
Claw-Evalpass^3978.375.257.344.2
Toolathlon-Verified1070.468.547.227.8
DeepPlanning1263.349.221.419.4
EnterpriseOps-Gym1241.040.230.527.3
YC-bench11$1,025,437$641,290$513,978$265,152
Tau3-Bench1248.848.533.718.6
ReasoningGPQA Diamond89.589.283.591.3
AIME 202695.294.594.789.2
AA-LCR84.082.083.372.0

1 Nanbeige4.3-33B is evaluated with preserve_thinking=true in the chat template. 2 Terminal-Bench 2.1 is evaluated using Terminus 2. 3 Terminal-Bench 4 and DeepSWE are evaluated using mini-swe-agent v2.4.6. 4 SWE-Atlas-QnA is evaluated using mini-swe-agent v2.4.6. Answers are evaluated against 1,304 task-specific rubrics using GPT-6 Sol as the judge, with medium reasoning effort. 5 NL2Repo is evaluated using Claude Code. 6 Automation Bench v1.0.6 includes 600 publicly released tasks. 7 JobBench is evaluated with the Claude Code harness equipped with a search tool. 8 GDPval is scored using revised rubrics. 9 Claw-Eval is evaluated exclusively on the Claw-Eval General subset (comprising 157 single-turn user interaction tasks). 10 Toolathlon-Verified sets no max_tokens limit for individual model requests. 11 YC-bench retains only the most recent 10 turns of user dialogue as historical context before each model call. 12 DeepPlanning, EnterpriseOps-Gym, Tau3-Bench are evaluated using their official repositories.

Nanbeige4.3-33B leads on most of the benchmarks, showing strong performance in both agentic coding and cowork tasks. Beyond its breadth across benchmarks, Nanbeige4.3-33B shows strong performance on agent tasks across a wide range of professions and industries. For example, on JobBench, it outperforms the baseline in most of the 35 occupations evaluated.

3. Quickstart

The tokenizer provides a configurable chat template for reasoning and tool-use scenarios:

  • enable_thinking controls whether the mod

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事件综述

Nanbeige4.3-33B 发布及 GGUF 量化版跟进

2026-10-26,Nanbeige 在 Hugging Face 发布 Nanbeige4.3-33B,定位为 Nanbeige4.3 系列旗舰教师模型,自称在约 30B 参数开源模型中智能体性能领先,并作为后续向 Nanbeige4.3-3B 等小模型蒸馏智能体能力的基础。模型基于 Nanbeige4.3-33B-Base 微调,Apache-2.0 许可,支持中英文。官方基准显示其 Terminal-Bench 2.1 得 80.9、DeepSWE 得 65.5、AIME 2026 得 95.2,多数基准领先 Qwen3.8-27B、Muse-Glimmer-30B 和 Gemma4-31B,但 SWE-Bench Pro 和 GDPval 落后于 Qwen3.8-27B;JobBench 在 35 个职业中的大多数项目优于基线。 随后(10-25 发布的社区帖,属后续进展),AtomicChat 发布该模型的 GGUF 量化文件,称其为 33B 稠密 Agent 与推理模型,Llama 架构、512K 上下文、默认开启思考模式。作者基于 BF16 原始权重自建重要性矩阵制作 AD(Atomic Dynamic)系列量化,大小从 13.7 GB 到 30.1 GB,并附各文件 KL 散度与 top-1 数据;指出该模型对量化较敏感,AD 文件在同体积下优于 llama.cpp 预设,如 AD-IQ4_XS(20.3 GB)的 KL 散度比 Q4_K_M(20.1 GB)低约三分之一,并建议 4 bit 以下量化仅在显存不足时使用。

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