CoSineVerifier-Tool-4B
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View details ↗DISCOVER & COMPARE
Explore AI models 11
Nanbeige4.1-3B
NanbeigeNanbeige4.1-3B is built upon Nanbeige4-3B-Base and represents an enhanced iteration of our previous reasoning model, Nanbeige4-3B-Thinking-2511, achieved through further post-training optimization with supervised fine-tuning (SFT) and reinforcement learning (RL). As a highly competitive open-source model at a small parameter scale, Nanbeige4.1-3B illustrates that compact models can simultaneously achieve robust **reasoning**, **preference alignment**, and **effective agentic behaviors**. We evaluate Nanbeige4.1-3B across a broad and diverse set of benchmarks covering **general reasoning**, and **deep-search capabilities**. On general reasoning tasks including **code**, **math**, **science**, **alignment**, and **tool-use** benchmarks, Nanbeige4.1-3B not only significantly outperforms same-scale models such as **Qwen3-4B**, but also demonstrates overall superior performance compared to larger models including **Qwen3-30B-A3B-2507** and **Qwen3-32B**.
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View details ↗Nanbeige4.2-3B
Nanbeige- 🎉 Nanbeige4.2-3B has taken the top spot in Artificial Analysis's latest leaderboard for small models. Nanbeige4.2-3B is a compact agentic model built on Nanbeige4.2-3B-Base, designed to combine strong agentic behavior with broad reasoning and alignment capabilities. Its Looped Transformer architecture reuses the transformer layers to increase model capacity without adding parameters. With only 3B non-embedding parameters, the model delivers solid performance on general-agent and code-agent tasks. During supervised fine-tuning (SFT), we expand the diversity of training environments through real-world environment integrations and large-scale environment synthesis. We further diversify task types, task assets, and the agentic scaffolds used for each task. To ensure training data quality, we apply filtering at both the trajectory and turn levels, combining test-case-based validation with rubric-based assessment. During reinforcement learning (RL), we combine outcome and process rewards to improve training stability for the compact model.
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View details ↗Nanbeige4.2-3B-Base
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4.2-3B-DSpark
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4.3-33B
NanbeigeIn 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). 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.
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View details ↗Nanbeige4.3-33B-Base
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4-3B-Base
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4-3B-Thinking-2510
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4-3B-Thinking-2511
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ToolMind-Web-3B
Nanbeige基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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