Descriptions:
Fahd Mirza locally installs and evaluates Nanbeige 4.1-3B, an open-source reasoning model from a Chinese AI research team whose name translates roughly to ‘North South Pavilion.’ Despite having only 3 billion parameters, the model claims benchmark performance exceeding Qwen 32B on alignment tasks and outperforming specialized 8B agentic models on deep search benchmarks — claims Mirza investigates firsthand.
The model is built on the Nanbeige 4.3B base and trained through supervised fine-tuning followed by reinforcement learning. It uses a standard dense transformer decoder architecture (no mixture-of-experts complexity) with a 131K token context window, keeping inference hardware requirements modest. Running on an Nvidia RTX 6000 with 48 GB of VRAM, the model loads in just over 8 GB — meaning it fits comfortably on a consumer 8 GB GPU.
Mirza runs three tests: a logical reasoning puzzle (the classic snail-climbing-pole problem), a creative coding task (a self-contained animated HTML aquarium screensaver), and a grid path-finding reasoning challenge. In all three cases the chain-of-thought traces are notably thorough for a 3B model — catching mid-calculation errors and validating answers via multiple methods. The one practical caveat Mirza flags is that the verbose thinking output may create unacceptable latency for real-time applications, though he considers this a minor tradeoff given the model’s overall capability-to-size ratio.
📺 Source: Fahd Mirza · Published February 21, 2026
🏷️ Format: Review
