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Nanbeige4.2-3B

by Nanbeige

3B-parameter open model from Nanbeige using a looped transformer, aimed at agentic and reasoning tasks.

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Nanbeige4.2-3B at a glance

What is Nanbeige4.2-3B?
Nanbeige4.2-3B is a model from Nanbeige. 3B-parameter open model from Nanbeige using a looped transformer, aimed at agentic and reasoning tasks.
Who makes Nanbeige4.2-3B?
Nanbeige4.2-3B is made by Nanbeige.
Does Nanbeige4.2-3B work in the EU?
Available in the EU. Described as an open-source model downloadable from ModelScope; no EU exclusion found, though the license was not confirmed on the fetched page. Last verified 14 Nov 2026.
How much does Nanbeige4.2-3B cost?
Open source. Downloadable from ModelScope; no pricing information found.
Where was Nanbeige4.2-3B shown?
In 1 creator video by AI Search. Most recently by AI Search on 26 Jul 2026, at 27:05 in “Claude Opus 5, GPT 6 hack, Flux 3, new Gemini, quantum breakthrough, new Qwen: AI NEWS”.

Sources: the vendor's own pages (linked above) and creator videos at the linked timestamps. EU status is information, not legal advice; see how we check.

About

Nanbeige4.2-3B is a small language model from Nanbeige, published on ModelScope. According to a YouTube creator's summary, it uses a looped transformer architecture that reuses layers, and it is reported to outperform larger Gemma 4 and Qwen 3.5 models on agentic and reasoning benchmarks. The model file is reported to be about 8 GB.

The ModelScope page describes it as an open-source model. The scraped page text did not include a model card, so details such as context length, training data, license and benchmark numbers could not be confirmed here and should be checked on the model page.

With 3B parameters it is small enough to run locally on consumer hardware, which suits on-device or low-cost agentic and reasoning experiments. Benchmark claims come from a third-party video summary and have not been independently verified.

Key features
  • 3B parameters
  • Looped transformer that reuses layers
  • Targets agentic and reasoning workloads
  • Reported ~8 GB model size
  • Distributed via ModelScope
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