Nanbeige4.2-3B

Model overview

- 🎉 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.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。