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

中国 AI 实验室在原生多模态前沿模型训练上出现分歧

中国 AI 实验室正根据原生多模态能力来区分其前沿模型。Moonshot AI 的 Kimi K3 和阿里巴巴的 Qwen3.8-Max 正在采用原生多模态训练,为复杂的代理任务定位。相比之下,DeepSeek V4、智谱 AI 的 Glm 4 和腾讯的 Hunyuan 仍仅支持文本,可能在未来的发展中落后。 AI

影响 中国前沿模型在多模态能力上的分歧可能导致专业化应用,并影响复杂 AI 代理任务的竞争格局。

排序理由 该条目讨论了中国各实验室前沿 AI 模型的发展和差异化策略,重点关注其技术能力(原生多模态训练 vs. 仅文本)。[lever_c_demoted from research: ic=1 ai=1.0]

在 Pandaily 阅读 →

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中国 AI 实验室在原生多模态前沿模型训练上出现分歧

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该条目讨论了中国各实验室前沿 AI 模型的发展和差异化策略,重点关注其技术能力(原生多模态训练 vs. 仅文本)。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    Kimi K3 与 DeepSeek V4 之间:原生多模态能力如何定义中国前沿模型的下一阶段

    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.