Nanbeige has published its Nanbeige4.3-33B model with a table of vendor benchmarks. A weekly AI brief dated October 26, 2026 says the weights were uploaded on October 24. The scores come from the vendor itself, so they are claims to check, not settled facts.
What the model card says
The model card describes a 33 billion parameter model under the Apache 2.0 license. It lists a context length of up to 524,288 tokens and support for English and Chinese. Nanbeige compares it with Qwen3.8-27B, Muse-Glimmer-30B and Gemma4-31B.
On Terminal-Bench 2.1, the card reports 80.9 for Nanbeige4.3-33B against 73.0 for Qwen3.8-27B. On EnterpriseOps-Gym it reports 41.0 against 40.2. That second gap is small.
The model does not lead everywhere. The card shows Qwen3.8-27B ahead on SWE-Bench Pro (61.7 against 60.3) and on GDPval (86.4 against 84.8). Gemma4-31B is ahead on GPQA Diamond.
What to check in a vendor result
The card is open about its test setup, which makes checking possible. It says some tests used specific agent harnesses, and that one coding benchmark was judged against 1,304 rubrics by another AI model. It also says all comparison models ran at their highest thinking effort with the same settings.
The weekly brief adds two cautions. It calls the numbers developer-reported. It also notes the card does not show the cost per successful task.
These points matter for anyone who relies on AI output:
- Find the owner of the number. A score published by the model maker has not been confirmed by anyone else.
- Read the test setup. A different harness or judge can change a score.
- Look for the losses. A table that shows where the model falls behind is easier to trust.
- Wait for independent runs. Outside testers can repeat the benchmarks.
What it means for data checking
Teams that use AI for analysis or reporting should treat a benchmark table as a lead, not proof. Run the model on a sample of your own data and compare its answers with known results. Our guide to AI output review shows a short check you can reuse. The same habit applies to data verification of any figure, and the trust index explains how we score evidence.
The model card also says no inference provider currently hosts the model. Anyone who wants to test it must run it themselves.