Nanbeige 4.2-3B

Not independently scored by Artificial Analysis — its base model Nanbeige/Nanbeige4.2-3B-Base is the closest proxy

A dense 3B from Nanbeige LLM Lab (BOSS Zhipin), pre-trained on ~23T tokens. Tiny — the "runs on anything" end of the table, laptop iGPU or CPU.

HumanEval pass@1 81% (13/16), via local GPU (transformers). Measured by us on this hardware, as a rough sanity check — HumanEval is a different, easier, partly-contaminated benchmark than Artificial Analysis’ composite, so it is not comparable to the coding-index column. Would not load in llama.cpp/Ollama at all — its custom 'nanbeige' architecture is unknown to them. We ran it via transformers with trust_remote_code in a GPU container, which needed an old transformers pin (for a rope_scaling change), sentencepiece, and a prebuilt flash_attn wheel. Worth it: it codes coherently and well for a 3B — the opposite of the Bonsai quants.

BigCodeBench-Hard pass@1 17% (20/121), via local GPU (transformers, no OpenAI endpoint). The brutal counterpart to HumanEval — where HumanEval saturates near the top, BCB-Hard spreads the field, so this is the number that actually separates coding ability. Floor — its verbose output (prose before code) overran the 2K budget on 145/148 of the comparable set. A weak draft coder on hard problems regardless.

~9 tok/s on RTX 4060 Ti, measured by us. RTX 4060 Ti via transformers+flash (bf16); ~1.0 tok/s CPU. Drops with length — the custom modeling's KV cache is ineffective (O(n^2)).

Won't load in llama.cpp / Ollama / LM Studio — the custom `nanbeige` architecture has no GGUF converter, so ignore the "runs on anything" first impression. Run it from safetensors with Hugging Face transformers and `trust_remote_code=True`.
- GPU: `pip install transformers==4.44.2` + the matching prebuilt flash-attn wheel; load with `attn_implementation="flash_attention_2"`, bf16.
- CPU: don't install flash-attn (it's CUDA-only). transformers still does a *static* import check, so drop a tiny empty `flash_attn` stub package on the path to satisfy it — `is_flash_attn_2_available()` is False on a CUDA-less torch, so the real import never fires. Load with `attn_implementation="eager"`, float32.
We measured ~9 tok/s on an RTX 4060 Ti and ~1 tok/s on a 24-core CPU. It's slower than a GGUF model its size because the custom modeling's KV cache is ineffective (throughput drops as the answer grows).

Q4 fits well under 6 GB with room for long context. Good as a fast draft / autocomplete model, not for hard agentic coding. Runs on Apple Silicon via MLX.