[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"aihot-art-92862":3},{"itemId":4,"vertical":5,"category":6,"source":7,"score":8,"title":9,"summary":10,"analysis":11,"url":12,"coverUrl":13,"direction":13,"marketSignal":13,"publishedAt":14},"92862","ai","技巧与观点","MarkTechPost",62,"Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2：开源万亿 MoE 模型对比","文章对比Kimi K3、DeepSeek V4 Pro和GLM-5.2三款开源万亿MoE模型的能力、许可与部署成本。","Kimi K3、DeepSeek V4 Pro和GLM-5.2三款开源万亿MoE模型对比，帮助开发者根据能力、许可和成本做出选择。\n· Kimi K3（2.8T参数）在Artificial Analysis Intelligence Index上得分约57，排名第三，仅次于Claude Fable 5和GPT-5.6 Sol。\n· DeepSeek V4 Pro（1.6T参数，49B活跃参数）得分44，GLM-5.2（744B参数，约40B活跃参数）得分51，GLM-5.2曾保持开源榜首直到K3发布。\n· 三款模型均支持百万Token上下文窗口，K3原生支持视觉，DeepSeek V4 Pro和GLM-5.2为纯文本。\n· 许可方面，DeepSeek V4 Pro采用宽松的MIT许可，K3和GLM-5.2各有特定条款，需注意商用限制。\n· 服务成本上，DeepSeek V4 Pro因活跃参数较少，推理成本相对更低；K3虽参数巨大但未公开活跃参数数，成本待定。\n看点：开源大模型竞争白热化，K3在性能上领先，但DeepSeek V4 Pro在成本和许可上更具优势，开发者需权衡选择。","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F07\u002F18\u002Fkimi-k3-vs-deepseek-v4-pro-vs-glm-5-2-open-trillion-scale-moe-models-compared-on-benchmarks-license-and-serving-cost\u002F",null,"2026-07-19 09:41:33"]