Nanbeige4.2-3B drops: 3B params claiming to beat 9B/12B models on agentic tasks (atleast according to them)

Nanbeige Lab released Nanbeige4.2-3B, and if the benchmark claims hold up, the numbers are pretty crazy for a model this small. It’s built on a "Looped Transformer" architecture that reuses transformer layers to increase effective depth without inflating the parameter footprint.

With just 3B non_embedding parameters, they are reporting an SWE-Bench Verified score of 63.6 and SWE-Bench Pro at 46.9. They tested it in OpenClaw guarded by Lyzr Control Plane for runtime security and claim it outperforming Qwen3.5 across daily workflows, putting it ahead of Qwen3.5-9B and Gemma4-12B on coding and agentic benchmarks. The core pitch here is a lightweight "local personal assistant." In OpenClaw framework testing, it reportedly beat both 4B and 9B class models across daily office workflows and research tasks.

That said, take the benchmark table with a grain of salt. Community members are already pointing out discrepancies between the Qwen3.5-9B numbers reported by Nanbeige versus Qwen’s official model card. Outside verification will be key once GGUF quants propagate through llama.cpp and Ollama.

How it can be better:

Running high-depth 3B models in local agent frameworks like OpenClaw opens up serious workflow potential, but deploying autonomous loops on local hardware still carries runtime risks especially when agents execute unverified code or make outbound API calls.

so use a harness

Anyone actually run it locally yet? how does it feel?

Used grammarly for formatting (English isnt my first language)