Nanbeige publishes Apache 2.0 Nanbeige4.3-33B coding model
First merged summary.
Nanbeige has released open 33B weights it says lead most of its own ~30B coding and cowork benchmarks, with a 3B distill planned.
Nanbeige LLM Lab, inside recruitment platform BOSS Zhipin, published Nanbeige4.3-33B and a base checkpoint on Hugging Face under Apache 2.0. The dense 33B Llama-style model is the flagship teacher for the Nanbeige4.3 series and targets agentic coding and professional cowork tasks, with a 524,288-token window and thinking on by default. The lab says it leads Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B on 17 of 20 self-run rows, including Terminal-Bench 2.1 at 80.9, while trailing Qwen on SWE-Bench Pro at 60.3 versus 61.7. MarkTechPost says several competitor figures conflict with those labs’ own model cards. A Nanbeige4.3-3B distillation is described as upcoming.
- Apache 2.0 weights include Nanbeige4.3-33B and Nanbeige4.3-33B-Base.
- Context window is 524,288 tokens; thinking is on by default.
- BF16 weights need about 66 GB; recipes use 4-way tensor parallelism.
- Terminal-Bench 2.1 is 80.9 versus 73.0 for Qwen3.8-27B.
- SWE-Bench Pro is 60.3, behind Qwen3.8-27B at 61.7.
- GPQA Diamond is 89.5, behind Gemma4-31B at 91.3; AIME 2026 is 95.2.
Timeline
dated events in the reporting · oldest first- Oct 24, 2026Nanbeige4.3-33B listed trending on Hugging Face · Hugging Face trending models
- Oct 25, 2026MarkTechPost reports Apache 2.0 33B and base release · MarkTechPost
What each outlet adds
beyond the shared facts- Hugging Face trending modelsTerminal-Bench 4.0, AIME, GPQA, JobBench and chat settings
- MarkTechPostApache 2.0, BOSS Zhipin, 524k context, 17 of 20, memory
- Vendors
- Nanbeige LLM LabNanbeige
- Organizations
- BOSS Zhipin
Coverage
oldest first · one row per article · UTCNanbeige releases Nanbeige4.3-33B agentic teacher model
The lab says the 33B model leads most ~30B coding and cowork benchmarks and will distill a 3B model.
Nanbeige releases Nanbeige4.3-33B open 33B agentic coding model
Apache 2.0 weights ship with a 524,288-token context and a base checkpoint; the lab says it leads 17 of 20 self-run benchmarks.