QClaw-4B

QClaw-4B

QClaw-4B is a 4-billion parameter language model fine-tuned for agentic tasks and tool use, designed for use with OpenClaw-compatible agent frameworks.

Despite its compact size, QClaw-4B achieves state-of-the-art results in the 4B class, matching or exceeding models several times larger — including Kimi K2.5 and GLM-4.5 — on the ClawBench agent benchmark.

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Benchmark Results

ClawBench

Leaderboard Comparison

Ru-Arena-General

QClaw-4B is SOTA in the ≤4B parameter class, outperforming Qwen 9B and other models more than twice its size.

Benchmark API tokens and evaluation cards provided by Aleksandr Nikolich — Love. Death. Transformers..

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Model Details

• Architecture: Decoder-only transformer
• Parameters: ~4B
• Benchmark suite: ClawBench
• Primary use case: Agentic workflows, tool calling, multi-step reasoning

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Training

QClaw-4B was trained on a curated mixture of:

• Agentic task trajectories (tool calling, multi-step planning)
• Instruction-following data
• Code and structured reasoning

Training annotation cards and dataset curation provided by Aleksandr Nikolich — Love. Death. Transformers..

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Intended Use

QClaw-4B is intended for:

• Agentic pipelines using OpenClaw or compatible frameworks
• Tool-augmented assistants requiring compact, efficient inference
• Research into small-model agent capabilities

Out-of-scope use: QClaw-4B is not intended for safety-critical systems without additional alignment work.

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Citation

@misc{qclaw4b2026,
  title  = {QClaw-4B: State-of-the-Art 4B Agent Model for OpenClaw},
  author = {Nikolay Kompanets (LakoMoor)},
  year   = {2026},
  url    = {https://huggingface.co/LakoMoor/QClaw-4B}
}

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License

Apache 2.0