1T-Param Chinese Multimodal LLM: OpenClaw's Enterprise Ally Open-Sourced

💡Open-source 1T-param multimodal LLM boosts enterprise OpenClaw—huge for Chinese AI stacks.

⚡ 30-Second TL;DR

What Changed

1 trillion parameters in multimodal LLM

Why It Matters

Empowers enterprise agents with massive multimodal capabilities affordably via open-source. Accelerates Chinese AI competitiveness globally.

What To Do Next

Download the model weights and test OpenClaw integration for multimodal agent tasks.

Who should care:Enterprise & Security Teams

Web-grounded analysis with 7 cited sources.

•Ant Group released Ling 2.5, a 1T-parameter multimodal LLM with hybrid attention architecture similar to Qwen3.5, potentially matching the described model[2].
•OpenClaw is an open-source, model-agnostic agent framework optimized for local inference of trillion-parameter LLMs using tools like vLLM and AMD hardware[5][6].
•Ling 2.5 integrates with OpenClaw via specific configurations like minimax_m2 tool parsers and 194K context windows for enterprise agent deployments[5].

📊 Competitor Analysis▸ Show

Model	Parameters	Architecture	Key Benchmarks
Ling 2.5 (OpenClaw Ally)	1T total	Hybrid Attention (MoE-like)	Frontier-level on reasoning/coding (comparable to S-tier)[1][2]
GLM-5 (Zhipu AI)	744B total (40B active)	MoE (256 experts)	77.8 SWE-bench, outperforms Gemini 3 Pro[3]
Kimi K2.5	Not specified (S-tier)	MoE	262K context, strong instruction-following[1]
MiniMax M2.5	Not specified (S-tier)	MoE	Matches proprietary on specific benchmarks[1]

•Hybrid attention architecture inspired by Qwen3.5 and Qwen3-Next, enabling efficient handling of 1T parameters[2].
•Supports 194K context window with configurations like --max-model-len 194000 and GPU utilization up to 0.99 for vLLM inference[5].
•Integrates reasoning-parser (minimax_m2_append_think) and tool-call-parser (minimax_m2) for OpenClaw agent workflows on AMD Ryzen AI hardware[5].

Accelerates enterprise AI adoption in China

Open-sourcing a 1T multimodal model tailored for OpenClaw lowers barriers for local inference and agent deployment in regulated sectors[5][6].

Pushes MoE efficiency standards

1T scale with hybrid attention rivals dense models in cost while matching S-tier benchmarks, influencing global open-source trends[1][2].

2026-02

OpenClaw gains traction as agent framework for trillion-parameter LLMs with AMD integration guides[5]

2026-02-23

Articles highlight OpenClaw's potential to transform LLM inference economics[6]

2026-03

Ling 2.5 1T-parameter multimodal LLM open-sourced as OpenClaw's enterprise partner[2]

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

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Original source: 量子位 ↗