MODEL PROFILE

LLaDA2.2-mini

inclusionAI · LLaDA2.2-mini

**LLaDA2.2-mini** is the lightweight variant of the agentic diffusion language model in the LLaDA2 series. Built upon the LLaDA2.0-mini architecture, it inherits the core innovations of the LLaDA2.2 series — **Levenshtein Editing** (introducing `DELETE` and `INSERT` control tokens) — enabling long-context tool calling, multi-turn interaction, and robust error correction, while maintaining a smaller parameter footprint and lower inference cost. For more details, please refer to our technical report. The following table compares **LLaDA2.0-mini**, **LLaDA2.1-mini**, and **LLaDA2.2-mini** across General and Agentic capabilities. General Function Calling BFCL v4 25.05 28.44 47.68 BFCL v3 70.72 72.06 69.02 Math AIME 2026 37.71 40.37 35.05 OlympiadBench 67.70 64.30 61.11 Coding LiveCodeBench v6 27.70 28.80 28.14 MultiPL-E 67.46 64.16 65.26 Instruction Following IFBench 32.33 31.60 24.93 Multi-IF 60.58 58.43 57.03 Reasoning KOR-Bench 49.92 46.64 43.60 Knowledge GPQA-Diamond 47.76 48.36 44.41 Long Context LongBench v2 15.51 12.13 34.99 General Average 45.68 45.03 46.47 Agentic Agent τ²-Bench - - 57.50 Claw-Eval - - 57.16 PinchBench - - 62.33 Agentic Average - - 59.00

Text generationlicense:apache-2.0
Context window131,072 Token
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 12:49:07
OVERVIEW

Model overview

**LLaDA2.2-mini** is the lightweight variant of the agentic diffusion language model in the LLaDA2 series. Built upon the LLaDA2.0-mini architecture, it inherits the core innovations of the LLaDA2.2 series — **Levenshtein Editing** (introducing `DELETE` and `INSERT` control tokens) — enabling long-context tool calling, multi-turn interaction, and robust error correction, while maintaining a smaller parameter footprint and lower inference cost. For more details, please refer to our technical report. The following table compares **LLaDA2.0-mini**, **LLaDA2.1-mini**, and **LLaDA2.2-mini** across General and Agentic capabilities. General Function Calling BFCL v4 25.05 28.44 47.68 BFCL v3 70.72 72.06 69.02 Math AIME 2026 37.71 40.37 35.05 OlympiadBench 67.70 64.30 61.11 Coding LiveCodeBench v6 27.70 28.80 28.14 MultiPL-E 67.46 64.16 65.26 Instruction Following IFBench 32.33 31.60 24.93 Multi-IF 60.58 58.43 57.03 Reasoning KOR-Bench 49.92 46.64 43.60 Knowledge GPQA-Diamond 47.76 48.36 44.41 Long Context LongBench v2 15.51 12.13 34.99 General Average 45.68 45.03 46.47 Agentic Agent τ²-Bench - - 57.50 Claw-Eval - - 57.16 PinchBench - - 62.33 Agentic Average - - 59.00

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。