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Chinese AI Labs Split on Native Multimodal Training for Frontier Models

By PulseAugur Editorial · [1 sources] ·

Chinese AI labs are differentiating their frontier models based on native multimodal capabilities. Moonshot AI's Kimi K3 and Alibaba's Qwen3.8-Max are adopting native multimodal training, positioning them for complex agent tasks. In contrast, DeepSeek V4, Zhipu AI's Glm 4, and Tencent's Hunyuan remain text-only, potentially lagging in future developments. AI

IMPACT The divergence in multimodal capabilities among Chinese frontier models may lead to specialized applications and influence the competitive landscape for complex AI agent tasks.

RANK_REASON The item discusses the development and differentiation strategies of frontier AI models from various Chinese labs, focusing on their technical capabilities (native multimodal training vs. text-only). [lever_c_demoted from research: ic=1 ai=1.0]

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Chinese AI Labs Split on Native Multimodal Training for Frontier Models

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The item discusses the development and differentiation strategies of frontier AI models from various Chinese labs, focusing on their technical capabilities (native multimodal training vs. text-only…
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COVERAGE [1]

  1. Pandaily TIER_1 English(EN) · [email protected] (Pandaily) ·

    Between Kimi K3 and DeepSeek V4: Why Native Multimodal Capability Defines the Next Phase of Chinese Frontier Models

    Moonshot AI Kimi K3, Alibaba Qwen3.8-Max, and ByteDance Doubao-Seed-2.1 commit to native multimodal training while DeepSeek, Zhipu, and Tencent Hunyuan stay text-only as vision-in-the-loop becomes decisive for long agent tasks.

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