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On October 26, 2026, Nanbeige (Nanbeige LLM Lab) released the open-weight model Nanbeige4.3-33B. According to a same-day report by MarkTechPost, the model was released on Hugging Face under the Apache 2.0 license; it is a 33B dense model aimed at agentic coding and office collaboration tasks, supports a 524,288-token context window, and was accompanied by the release of Nanbeige4.3-33B-Base, which supports customizable post-training. The report stated that in the lab's own comparison table, it led in 17 of 20 benchmarks against Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B, scoring 80.9 on Terminal-Bench 2.1, but trailed Qwen3.8-27B on SWE-Bench Pro with 60.3 versus 61.7; the article also noted that some competitor figures in its table were inconsistent with the official model cards, that two SWE-Bench Pro entries appeared to be swapped, and that the results await independent reproduction. Another report stated that the model surpasses Qwen 27B on most agentic benchmarks, achieves SOTA agentic performance among open-source models at roughly the 30B parameter scale, and can handle real tasks at relatively low inference cost after integration with mainstream agent frameworks. The model is positioned as Nanbei
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Anthropic Files S-1 for IPO: $16.3 Billion in First-Half Revenue, $974 Million in Q2 Operating ProfitDevelopments
1 confirmed developments
- Source:Thomas Wolf
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Browse reports and discussion by time. Their evidence levels remain distinct.
Oct 27
Latent SpaceSelected
Anthropic Files S-1 for IPO: $16.3 Billion in First-Half Revenue, $974 Million in Q2 Operating Profit
Anthropic publicly filed its S-1 on Monday, planning to list on Nasdaq under the ticker ANT, with revenue growing from $41 million in 2023 to $16.3 billion in the first half of 2026, including $11.6 billion in Q2 alone, up 1368% year over year. Q2 operating profit was $974 million, first-half operating cash flow was positive at $3.9 billion, while the $80.7 billion net loss mainly came from $81.1 billion in non-cash financial instrument expenses. About 48% of first-half revenue (roughly $7.8 billion) flowed through Bedrock, Vertex, and Azure Marketplace; the S-1 also disclosed that pretraining of a frontier model was recently delayed, and listed NVIDIA compute commitments of up to $120.5 billion.
Oct 26
Thomas Wolf
BOSS Zhipin research team Nanbeige releases open-source 33B model Nanbeige4.3-33B
Nanbeige, a research team under BOSS Zhipin, has released the open-weight model Nanbeige4.3-33B, outperforming Qwen 27B on most agent benchmarks. The model achieves SOTA agentic performance among open-source models at roughly the 30B parameter scale, shows strong results on agentic coding and collaboration benchmarks, and after integration with mainstream agent frameworks can handle real-world tasks at lower inference cost. It will also serve as a teacher model for the subsequent smaller Nanbeige4.3-3B.
MarkTechPost
Nanbeige Releases Nanbeige4.3-33B Open-Source Agentic Coding Model
Nanbeige LLM Lab released Nanbeige4.3-33B on Hugging Face under the Apache 2.0 license. It is a 33B dense model aimed at agentic coding and office collaboration tasks, supports a 524,288 token context window, and is released alongside Nanbeige4.3-33B-Base, which supports customizable post-training. In the lab's self-built comparison table, it leads in 17 of 20 benchmarks against Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B, scoring 80.9 on Terminal-Bench 2.1, but trails Qwen3.8-27B on SWE-Bench Pro with 60.3 versus 61.7. The article points out that some competitor figures in its table are inconsistent with the official model cards from Meta and Google, and that two SWE-Bench Pro entries appear to be swapped; these results need to await independent reproduction. The model is positioned as the teacher model for the Nanbeige4.3 series, with plans to subsequently distill agentic capabilities into Nanbeige4.3-3B.
Testing Catalog
Nanbeige Releases the Open-Source Nanbeige4.3-33B Model
Nanbeige has released Nanbeige4.3-33B, claiming that it achieves SOTA agent performance among open-source models with roughly 30B parameters, with outstanding results on agentic coding and cowork tasks. The model surpasses Qwen3.8-27B, Muse-Glimmer-30B, and Gemma4-31B on most benchmarks, and once integrated into mainstream agent frameworks, it can handle real-world tasks at relatively low inference cost. The company also plans to release a smaller Nanbeige4.3-3B, with the 33B version serving as its teacher model.
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