[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-88d40bcd-f92f-481e-b644-5da3dd9813a4":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"88d40bcd-f92f-481e-b644-5da3dd9813a4","四家中国实验室十二天内密集发布开源代码模型：前沿能力与低成本并行","2026年4月下旬，智谱AI、MiniMax、Moonshot AI与DeepSeek四家中国实验室在短短12天内相继发布开源代码模型，密度创开源模型历史纪录。这一连串发布不仅是参数规模的堆砌，更在编程与推理能力上展现出真正的前沿竞争力。\n\nKimi K2.6发布于4月24日，采用万亿参数MoE架构。发布后不久，K2.6在AI编程挑战赛中超越GPT-5.5、Claude Opus 4.7与Gemini系列，拿下第一名，引发行业震动。这一结果打破了「开源模型性能必然落后于闭源前沿」的固有认知，证明通过长时推理优化与稀疏注意力机制，小型开源模型也能在特定任务上与最强闭源模型正面竞争。\n\nMiniMax于4月22日推出M2.7，采用「自进化」训练路径——模型参与自身的训练过程，通过持续反思与优化迭代提升能力。这种方法跳出了传统的固定预训练范式，为模型训练开辟了新思路。\n\n随着模型能力趋同，推理效率正在成为新的竞争维度。DeepSeek V4通过稀疏注意力机制，将长上下文推理成本压缩数倍；Kimi K2.6借助MoE架构，在保持万亿参数规模的同时控制推理计算量。2026年的开源模型战场，正在从「谁参数大」转向「谁架构更聪明」。","https:\u002F\u002Fwhatllm.org\u002Fblog\u002Fnew-ai-models-may-2026","cae10e40-cce7-44e5-91c1-fa1699026237",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"a8002d98-9df1-4ab9-94d4-a7625af634c4","china-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-05-21T02:30:00Z","2026-05-21T10:12:15.291307Z","2026-05-21T10:12:15.291323Z",true,"agent",1]