{
  "text": "大家好 欢迎收看新一期视频\nDeepseek刚刚发布了一个很有意思的更新\nDeepseek V4 Flash官方API现已开启公测\n新版本命名为Deepseek V4 Flash 0731\n这可不只是一个小补丁\nDeepseek表示他们大幅提升了智能体能力\n在该模型上甚至\n使其测试分数如今已远超V4 Pro预览版\n也就是说\n这个小模型现在击败了预览版\n极大模型在智能体任务上的版本\n这确实挺疯狂的\n我也用我自己的Kinbench测试了它\n而在我的基准测试中\n它做到了\n其他任何模型都未曾做到的事\n包括Code 3.5\n以及Opus稍后我们会细聊\n但首先\n我们先聊聊这次更新到底是什么\n前阵子我介绍过DeepSeek V4 Pro\n和V4 Flash的预览版\n包括如何通过NVIDIA Link免费使用它们\n视频链接已放在下方\n我们先快速回顾一下Flash\n它是V4系列中较小的模型\n该混合专家模型拥有2000\n0840亿的总参数量\n约130亿激活参数\n支持100万Token上下文窗口\n它定位为主打快速\n与低成本版本\n而V4 Pro\n是1.6万亿参数旗舰大模型\n关于这次0731更新\n有一个关键细节\nDeepSea明确表示\nV4Flash 0731\n保持了完全相同的模型架构\n和参数规模与预览版完全一致\n所以它并没有变大\n依然是\n那款2840亿参数的Flash\n但在此基础上\n它似乎进行了高强度的智能体后训练\n所有的性能提升都源于训练的优化\n而不是增加参数量\n他们还明确指出\n这次升级仅是用于DeepSeek V4 Flash API\nV4 Pro API\n以及App端D模型\n和网页端目前都保持不变\n公告最后提到\nDeepSeek V4 Pro的正式版本\n很快就会发布\n所以这次Flash更新基本只是热身\n真正的重头戏还在后头\n另外\n这次发布中还有一点\n我觉得非常明智\n官方V4Flash现在原生支持Response API格式\nDipsy还表示他已完全适配Codex\n所以他们不仅是发布了一个模型\n而不是被动等待工具链的适配\n他们直接面向Codex适配框架进行了开发\n这意味着你可以将其接入\n到Codex风格的工作流中且毫无阻碍\nGLM以及国内的其他一些实验室也在做\n类似的框架适配\n事实证明效果立竿见影\n这确实是个明智之举\n接下来看看官方的基准测试数据\n这次性能提升太惊人了\n在Terminal Bench 2.1测试中\n新版Flash跑分高达82.7\n而此前的预览版仅为61.8\n甚至连V4 Pro预览版也只有72.1分\n这也让它超越了81分的GLM40520\n而且距离85分的OPUS差距并不大\n要知道后者的价格可要贵得多\n而最夸张的是SWBEC\n预览版在这个测试中仅获得了7.3分\n而新版本直接飙升到54.4分\n实现了从基本无法使用\n到足以于46.2分的GLM40520正面竞争的水平\n甚至是58分的OPUS\nCybergen也从38.7分\n升至76.7分涂了冷验证机\n从49.7分升至70.3分\n在其内部DSBench测试集上\n全战任务得分从37升至68.7\n高难度编码智能体任务也\n从25.8升至59.6\n总的来说\n这叫其自身的预览板有了巨大飞跃\n且在大多数智能体基准测试中\n它已逼近OPUS 4.8\n而它作为一款尺寸小得多\n且更便宜的模型\n不过需要说明的是\nDeep-C指出\n在公开的编码智能体任务测试中\nV4 Flash使用了\n其即将推出的Deep-C Harness框架在极简模式下\n以最高档位运行\n且套采样设为0.95\n温度参数设为一可见\n测试框架的选择至关重要\n测试结果可能不同\n数据仅供参考\n不过了解本频道的人都知道\n我们从不只看官方宣传数据\n所以我用自己的基准测试跑了一下\n话不多说我们直接切入正题\n和往常一样\n我用我的TingBench进行了测试\n其中涵盖了一些前端\n与动画一些FreeJS任务以及SVG测试\n一道数学题以及一个长城智能体任务\n最后还有一个超难的3D任务\n每项测试满分10分\n最后我们会通过最终的图表来看看\n看看它的表现与其他模型相比如何\n第一题是电梯模拟\n模型需要构建一个模拟程序\n你可以在不同楼层生成乘客\n共有三部电梯\n其每部只能在一人\n没赶上的人需要搭乘下一班电梯\n还需要有提示框显示每个人的目标楼层\n当鼠标悬停在他们身上时\n我们发送提示词看看效果\n这个表现只能算一半\n得了五分基本框架是有了\n但关于乘客没赶上电梯\n并被下一班电梯接走的核心逻辑并没有完全跑通\n而且动画也不够流畅\n作为参考\n只有OPUS模型在这项测试中拿过10分\n而像CLUD3.5这样的模型\n和KIMMYK3也只有9分\n所以这开局只能算重归重矩\n第二个测试是FreeJF隐形眼镜盒\n它需要生成一个3D模型\n是一个带有明显L和R标识盖子的隐形眼镜盒\n并且点击盖子能够将其打开这项测试\n几乎所有模型都会在这里翻车\n我们发送过去看看\n好的 这其实做得非常好\n它拿到了8分\n盒子看起来很像样\n像个合格的眼镜盒L\n而且L和二盖也清晰可见\n点击打开的交互也有效\n这让他与Quan2.5Maps并列\n而Opus拿到了5分\n是历史第二高分\n在该测试中仅次于Plus 3.5\n他仍然是唯一在这个测试中\n拿到满分10分的模型\n所以对于一个小模型来说\n能有这样的表现已经非常惊艳了\n第三题是\nFreeJF折叠桌\n这里有一个滑块\n当你向右拖动它时\n桌子应该展开\n向左拖则折叠回去\n动画需要无缝衔剑\n且呈现出真正的3D效果\n我们发送请求看看\n这个得到了7分\n桌子确实能折叠起来\n并能随滑快拖动而展开\n过度基本自然\n只是还不够完全流畅\n表现最好的模型\n如Cloud 3.5\nKIMI GLM4\n以及Summit 3.5\n在这项测试中都拿到了9分\n但对比来看\nOpus在这项测试中仅得了5分\n所以这表现绝对算很不错了\n第四题是生成熊猫的SVG\n吃着汉堡\n我们发送过去看看\n好\n这确实能看出是一只熊猫\n也能看出是汉堡\n但构图有点别扭\n而且看起来不太像在吃汉堡\n这题给五分\n在这项测试中\n像Pimi和Gemini 1.5这样表现最好的模型\n以及Quain 2.5 Max得分也仅为8分\n所以并不算惊艳\n第五题是制作弓箭\n模拟游戏里面设计了四个靶子\n玩家需要以最短的时间击中所有靶子\n从而登上排行榜\n我们发送代码运行一下看看\n这道题得了7分\n核心射击机制可以正常运行\n靶子能判定击中\n排行榜功能也正常\n但整体手感和细节打磨\n还达不到顶尖模型的水平\n比如Grog 1.5和Quang 2.5 Math\n虽然Opus模型在这里都拿了满分\n但这依然是个可玩的游戏\n所以表现还算不错\n第六题是一道数学难题\n关于计算有序队的排列数\n答应该是2460\n很多模型在这一题上完全翻车\n我们提交一下\n看看\n他完美答对了2460\n完全正确\n拿了满分10分\n这让他机身同一梯队\n与Cloud 3.5\nTimmy Chat\nQueen Max\n以及Opus模型并列\n有趣的是\n在我测试时\nV4 Pro预览版也做对了这道题\n所以V4系列的推理能力显然非常过瘾\n第七题是一个复杂的长城任务\n生成一个关于熊猫知识的数据集\n并用该数据集为调一个GEM2B模型\n接着构建一个本地WebUI每次刷新页面都会生成一条熊猫冷知识\n全程从零开始完全自主完成\n且完全在本地运行\n我们启动任务看看效果\n这次的表现绝对可以达满分\n它生成了数据集并完成了整个微调\n独自运行期间完全没有卡科\n生成的Web UI正常运行\n每次刷新都显示新的熊猫冷知识\n这正是那种复杂的长程智能体任务\nDeep C声称对其进行过针对性升级\n说实话\n效果显而易见\n之前的预览版远没这么可靠\n这波表现确实堪称完美\n第八题也是最后一题\n是制作一个3D万秒\n要求是一个功能完整的3D万秒\n能显示当前实际时间\n秒针 分针\n以及时针都要平滑转动\n还要有日期\n核心器显示\n以及双时区\n这是整个测试集中最难的一题\n之前没有任何模型\n能在这道题上拿到好成绩\n历史最高分是Fibo5创下的\n4分\n让我们直接运行测试\n但你感谢\nLip6 V4 Flav竟然拿到了6分\n创下这道题历史记录\n手表完整呈现出来\n指针转动平滑\n只能实时同步时间\n双时区设置也基本正常\n虽然还不完美\n所以没能拿到更高的分数\n但这个小模型刚刚击败了\nCloud 3.5 Opus\n以及我测试集最难题目上的\n其他所有前沿模型\n这完全出乎我的意料\n来看一下最终的图表\nDeep Seek V4 Flash 0731\n最终拿到了58分\n得分率为72.5%\n这让它排在\n略高于GPT4O513的71.25%\n稍低于Gemini 1.5 Pro的75%\n而在它之上\n有Kinning和\nOpas并列77.5%\nGTT 450808达80%\n接着是Quain\nQuain 2.5 Max拿下81.25%\n而Cloud 3.5仍以82.5%稳居榜首\n而对比以下背景\n你就能明白这有多惊人\n我之前测试Deep Seek V4 Pro预览版时\n它在此测试中仅得了24.8%\n新的Flash模型得分达到了72.5%\n这意味着该系列的小模型现在\n比大模型预览版的表现提升了近三倍\n在我的测试中\n而且在架构和尺寸与预览版相同\n单靠后训练就能取得惊人提升\n不可思议\n这也印证了Deep Seed所宣称的Agent能力升级\n从得分规律中也可以清楚地看到\n所有射击推理\n或长城Agent任务基本上都拿到了满分\n比如数学和微调任务\n还有创纪录的时钟生成常识\n但像熊猫SVG这种纯视觉打磨的任务\n还有电梯动画\n在这些方面它的表现依然平平\n所以这确实很像是一个\n针对智能体编程深度微调的模型\n正如官方基准测试所示\n那么结论是什么\n这绝对是目前性价比最高的模型之一\n可用于智能体编程\n在我的测试中\n它的表现已经达到了Gemini 1.5 Pro的水平\n它可以直接接入Cursor等代码工具\n这得益于其API知识\n而且Ditsig的价格\n一直都非常便宜\n如果你是学生\n或者对于预算有限的开发者\n这绝对是个极佳的选择\n他显然还无法击败Cloud 3.5\n或是在综合表现上超越OPUS\n而且他的视觉能力依然表现平平\n但考虑到他的体量和价格\n这已经足够经验了\n而且别忘了\n这还仅仅是Flash版本\n官方的DeepSick V4 Pro也即将发布\n据官方介绍\n如果将同样的智能体后训练应用\n到1.6万亿参数的模型上\n那对市面上昂贵的B元模型来说\n将是降维打击般的存在\n我会在上线后第一时间进行测试\n尽情关注\n总的来说\n这非常酷",
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      "text": "大家好 欢迎收看新一期视频",
      "chunk": 0,
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      "text": "Deepseek刚刚发布了一个很有意思的更新",
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      "text": "Deepseek V4 Flash官方API现已开启公测",
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      "text": "新版本命名为Deepseek V4 Flash 0731",
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      "text": "这可不只是一个小补丁",
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      "start": 21.6,
      "end": 25.0,
      "text": "Deepseek表示他们大幅提升了智能体能力",
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      "end": 27.18,
      "text": "在该模型上甚至",
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      "start": 27.18,
      "end": 30.6,
      "text": "使其测试分数如今已远超V4 Pro预览版",
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      "text": "也就是说",
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      "start": 31.78,
      "end": 33.98,
      "text": "这个小模型现在击败了预览版",
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      "end": 36.72,
      "text": "极大模型在智能体任务上的版本",
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      "text": "这确实挺疯狂的",
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      "text": "我也用我自己的Kinbench测试了它",
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      "text": "而在我的基准测试中",
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      "start": 42.32,
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      "text": "它做到了",
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      "start": 43.04,
      "end": 45.2,
      "text": "其他任何模型都未曾做到的事",
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      "text": "包括Code 3.5",
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      "end": 48.36,
      "text": "以及Opus稍后我们会细聊",
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      "text": "但首先",
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    {
      "start": 48.86,
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      "text": "我们先聊聊这次更新到底是什么",
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      "text": "前阵子我介绍过DeepSeek V4 Pro",
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      "start": 53.34,
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      "text": "和V4 Flash的预览版",
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      "start": 55.12,
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      "text": "包括如何通过NVIDIA Link免费使用它们",
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      "text": "视频链接已放在下方",
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      "text": "我们先快速回顾一下Flash",
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      "text": "它是V4系列中较小的模型",
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      "start": 63.12,
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      "text": "该混合专家模型拥有2000",
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      "text": "0840亿的总参数量",
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      "text": "约130亿激活参数",
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      "text": "支持100万Token上下文窗口",
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      "start": 72.0,
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      "text": "它定位为主打快速",
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      "start": 73.78,
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      "text": "与低成本版本",
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      "start": 75.9,
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      "text": "是1.6万亿参数旗舰大模型",
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    {
      "start": 78.2,
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      "text": "关于这次0731更新",
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      "text": "有一个关键细节",
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      "text": "DeepSea明确表示",
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      "text": "V4Flash 0731",
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      "start": 84.34,
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      "text": "保持了完全相同的模型架构",
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      "text": "所以它并没有变大",
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      "text": "那款2840亿参数的Flash",
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      "text": "它似乎进行了高强度的智能体后训练",
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      "text": "而不是增加参数量",
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      "text": "这次升级仅是用于DeepSeek V4 Flash API",
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      "start": 106.48,
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      "text": "以及App端D模型",
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      "text": "和网页端目前都保持不变",
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      "start": 110.12,
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      "text": "公告最后提到",
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      "text": "DeepSeek V4 Pro的正式版本",
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      "start": 114.24,
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      "text": "很快就会发布",
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      "text": "另外",
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      "text": "我觉得非常明智",
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      "text": "Dipsy还表示他已完全适配Codex",
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      "text": "而不是被动等待工具链的适配",
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      "text": "这意味着你可以将其接入",
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      "start": 142.98,
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      "text": "GLM以及国内的其他一些实验室也在做",
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      "text": "事实证明效果立竿见影",
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      "start": 148.84,
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      "text": "这确实是个明智之举",
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      "text": "接下来看看官方的基准测试数据",
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      "text": "这次性能提升太惊人了",
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      "text": "新版Flash跑分高达82.7",
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      "text": "而此前的预览版仅为61.8",
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      "text": "这也让它超越了81分的GLM40520",
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      "text": "而且距离85分的OPUS差距并不大",
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      "text": "要知道后者的价格可要贵得多",
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      "text": "而最夸张的是SWBEC",
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      "start": 172.98,
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      "text": "预览版在这个测试中仅获得了7.3分",
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    {
      "start": 175.58,
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      "text": "而新版本直接飙升到54.4分",
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      "start": 178.04,
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      "text": "实现了从基本无法使用",
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    {
      "start": 179.9,
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      "text": "到足以于46.2分的GLM40520正面竞争的水平",
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    {
      "start": 183.9,
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      "text": "甚至是58分的OPUS",
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      "start": 185.92,
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      "text": "Cybergen也从38.7分",
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    {
      "start": 188.14,
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      "text": "升至76.7分涂了冷验证机",
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      "start": 190.68,
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      "text": "从49.7分升至70.3分",
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      "start": 193.28,
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      "text": "在其内部DSBench测试集上",
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      "start": 195.5,
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      "text": "全战任务得分从37升至68.7",
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      "start": 198.26,
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      "text": "高难度编码智能体任务也",
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      "text": "从25.8升至59.6",
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      "start": 202.74,
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      "text": "总的来说",
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      "start": 203.8,
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      "text": "这叫其自身的预览板有了巨大飞跃",
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      "text": "且在大多数智能体基准测试中",
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      "text": "它已逼近OPUS 4.8",
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      "start": 209.82,
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      "text": "而它作为一款尺寸小得多",
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      "start": 211.82,
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      "text": "且更便宜的模型",
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      "text": "不过需要说明的是",
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      "start": 214.1,
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      "text": "Deep-C指出",
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      "start": 215.12,
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      "text": "在公开的编码智能体任务测试中",
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      "text": "V4 Flash使用了",
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      "start": 218.38,
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      "text": "其即将推出的Deep-C Harness框架在极简模式下",
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    {
      "start": 221.36,
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      "text": "以最高档位运行",
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      "start": 222.76,
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      "text": "且套采样设为0.95",
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      "start": 224.12,
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      "text": "温度参数设为一可见",
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      "start": 225.84,
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      "text": "测试框架的选择至关重要",
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    {
      "start": 227.5,
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      "text": "测试结果可能不同",
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    {
      "start": 229.0,
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      "text": "数据仅供参考",
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      "start": 230.58,
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      "text": "不过了解本频道的人都知道",
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      "text": "我们从不只看官方宣传数据",
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      "start": 234.64,
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      "text": "所以我用自己的基准测试跑了一下",
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      "start": 236.38,
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      "text": "话不多说我们直接切入正题",
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      "start": 238.36,
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      "text": "和往常一样",
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      "text": "我用我的TingBench进行了测试",
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      "start": 240.86,
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      "text": "其中涵盖了一些前端",
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      "start": 242.4,
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      "text": "与动画一些FreeJS任务以及SVG测试",
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    {
      "start": 245.2,
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      "text": "一道数学题以及一个长城智能体任务",
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    {
      "start": 248.78,
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      "text": "最后还有一个超难的3D任务",
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      "start": 250.72,
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      "text": "每项测试满分10分",
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    {
      "start": 252.5,
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      "text": "最后我们会通过最终的图表来看看",
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      "start": 255.28,
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      "text": "看看它的表现与其他模型相比如何",
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      "text": "第一题是电梯模拟",
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      "text": "模型需要构建一个模拟程序",
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      "text": "你可以在不同楼层生成乘客",
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      "text": "共有三部电梯",
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      "start": 266.02,
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      "text": "其每部只能在一人",
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    {
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      "text": "没赶上的人需要搭乘下一班电梯",
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    {
      "start": 269.98,
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      "text": "还需要有提示框显示每个人的目标楼层",
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    {
      "start": 272.76,
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      "text": "当鼠标悬停在他们身上时",
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    {
      "start": 274.72,
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      "text": "我们发送提示词看看效果",
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    {
      "start": 276.5,
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      "text": "这个表现只能算一半",
      "chunk": 0,
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    },
    {
      "start": 278.02,
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      "text": "得了五分基本框架是有了",
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    },
    {
      "start": 280.0,
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      "text": "但关于乘客没赶上电梯",
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    {
      "start": 282.02,
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      "text": "并被下一班电梯接走的核心逻辑并没有完全跑通",
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    {
      "start": 284.62,
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      "text": "而且动画也不够流畅",
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    {
      "start": 286.5,
      "end": 287.36,
      "text": "作为参考",
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      "no_speech_prob": 1.68271663963937e-11,
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    {
      "start": 287.36,
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      "text": "只有OPUS模型在这项测试中拿过10分",
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      "start": 289.86,
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      "text": "而像CLUD3.5这样的模型",
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      "start": 291.8,
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      "text": "和KIMMYK3也只有9分",
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    {
      "start": 293.62,
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      "text": "所以这开局只能算重归重矩",
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    {
      "start": 295.54,
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      "text": "第二个测试是FreeJF隐形眼镜盒",
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      "start": 298.64,
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      "text": "它需要生成一个3D模型",
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    {
      "start": 301.24,
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      "text": "是一个带有明显L和R标识盖子的隐形眼镜盒",
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    {
      "start": 304.94,
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      "text": "并且点击盖子能够将其打开这项测试",
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      "start": 308.66,
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      "text": "几乎所有模型都会在这里翻车",
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    {
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      "text": "我们发送过去看看",
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      "start": 312.48,
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      "text": "好的 这其实做得非常好",
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      "start": 314.8,
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      "text": "它拿到了8分",
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      "text": "盒子看起来很像样",
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      "text": "像个合格的眼镜盒L",
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      "start": 319.3,
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      "text": "而且L和二盖也清晰可见",
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      "start": 321.8,
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      "text": "点击打开的交互也有效",
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      "start": 323.24,
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      "text": "这让他与Quan2.5Maps并列",
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      "start": 325.08,
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      "text": "而Opus拿到了5分",
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    {
      "start": 326.42,
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      "text": "是历史第二高分",
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      "start": 327.34,
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      "text": "在该测试中仅次于Plus 3.5",
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    {
      "start": 329.52,
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      "text": "他仍然是唯一在这个测试中",
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    {
      "start": 331.14,
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      "text": "拿到满分10分的模型",
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      "start": 332.18,
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      "text": "所以对于一个小模型来说",
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      "start": 333.86,
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      "text": "能有这样的表现已经非常惊艳了",
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      "start": 335.64,
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      "text": "第三题是",
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      "text": "FreeJF折叠桌",
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      "start": 338.58,
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      "text": "这里有一个滑块",
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      "start": 339.86,
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      "text": "当你向右拖动它时",
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      "text": "桌子应该展开",
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    {
      "start": 343.4,
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      "text": "向左拖则折叠回去",
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    {
      "start": 344.78,
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      "text": "动画需要无缝衔剑",
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    {
      "start": 346.48,
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      "text": "且呈现出真正的3D效果",
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    {
      "start": 348.5,
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      "text": "我们发送请求看看",
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      "start": 349.78,
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      "text": "这个得到了7分",
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      "start": 351.26,
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      "text": "桌子确实能折叠起来",
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    {
      "start": 352.78,
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      "text": "并能随滑快拖动而展开",
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    {
      "start": 355.22,
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      "text": "过度基本自然",
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    {
      "start": 356.82,
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      "text": "只是还不够完全流畅",
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      "start": 358.4,
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      "text": "表现最好的模型",
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    {
      "start": 360.16,
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      "text": "如Cloud 3.5",
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    {
      "start": 361.16,
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      "text": "KIMI GLM4",
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      "start": 362.34,
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      "text": "以及Summit 3.5",
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    {
      "start": 363.8,
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      "text": "在这项测试中都拿到了9分",
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    {
      "start": 365.44,
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      "text": "但对比来看",
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    {
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      "text": "Opus在这项测试中仅得了5分",
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    {
      "start": 368.46,
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      "text": "所以这表现绝对算很不错了",
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      "start": 370.74,
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      "text": "第四题是生成熊猫的SVG",
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    {
      "start": 373.06,
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      "text": "吃着汉堡",
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    {
      "start": 374.22,
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      "text": "我们发送过去看看",
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      "start": 375.42,
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      "text": "好",
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      "start": 376.5,
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      "text": "这确实能看出是一只熊猫",
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    {
      "start": 378.62,
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      "text": "也能看出是汉堡",
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    {
      "start": 380.42,
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      "text": "但构图有点别扭",
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      "start": 381.86,
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      "text": "而且看起来不太像在吃汉堡",
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    {
      "start": 384.62,
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      "text": "这题给五分",
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    {
      "start": 386.02,
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      "text": "在这项测试中",
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    {
      "start": 388.0,
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      "text": "像Pimi和Gemini 1.5这样表现最好的模型",
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    {
      "start": 391.6,
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      "text": "以及Quain 2.5 Max得分也仅为8分",
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    {
      "start": 395.24,
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      "text": "所以并不算惊艳",
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      "start": 397.34,
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      "text": "第五题是制作弓箭",
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    {
      "start": 399.18,
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      "text": "模拟游戏里面设计了四个靶子",
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      "start": 401.94,
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      "text": "玩家需要以最短的时间击中所有靶子",
      "chunk": 0,
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      "avg_logprob": -0.10077982041442278,
      "no_speech_prob": 1.3044664307071407e-11,
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    },
    {
      "start": 404.7,
      "end": 406.12,
      "text": "从而登上排行榜",
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      "avg_logprob": -0.10077982041442278,
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    },
    {
      "start": 406.12,
      "end": 407.74,
      "text": "我们发送代码运行一下看看",
      "chunk": 0,
      "language": "zh",
      "avg_logprob": -0.10077982041442278,
      "no_speech_prob": 1.3044664307071407e-11,
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    {
      "start": 407.74,
      "end": 409.14,
      "text": "这道题得了7分",
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    {
      "start": 409.14,
      "end": 410.96,
      "text": "核心射击机制可以正常运行",
      "chunk": 0,
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    },
    {
      "start": 410.96,
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      "text": "靶子能判定击中",
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    {
      "start": 412.66,
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      "text": "排行榜功能也正常",
      "chunk": 0,
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    },
    {
      "start": 414.04,
      "end": 416.2,
      "text": "但整体手感和细节打磨",
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    },
    {
      "start": 416.2,
      "end": 417.84,
      "text": "还达不到顶尖模型的水平",
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    },
    {
      "start": 417.84,
      "end": 420.74,
      "text": "比如Grog 1.5和Quang 2.5 Math",
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    {
      "start": 420.74,
      "end": 423.56,
      "text": "虽然Opus模型在这里都拿了满分",
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    {
      "start": 423.56,
      "end": 425.1,
      "text": "但这依然是个可玩的游戏",
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    {
      "start": 425.1,
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      "text": "所以表现还算不错",
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    {
      "start": 426.96,
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      "text": "第六题是一道数学难题",
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    },
    {
      "start": 429.24,
      "end": 431.32,
      "text": "关于计算有序队的排列数",
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      "avg_logprob": -0.10077982041442278,
      "no_speech_prob": 1.3044664307071407e-11,
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    },
    {
      "start": 431.32,
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      "text": "答应该是2460",
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      "compression_ratio": 1.1253918495297806,
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    {
      "start": 433.3,
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      "text": "很多模型在这一题上完全翻车",
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    {
      "start": 435.74,
      "end": 436.62,
      "text": "我们提交一下",
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    {
      "start": 436.62,
      "end": 437.7,
      "text": "看看",
      "chunk": 0,
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      "no_speech_prob": 1.1701846956702067e-11,
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    },
    {
      "start": 437.7,
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      "text": "他完美答对了2460",
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    {
      "start": 439.86,
      "end": 441.42,
      "text": "完全正确",
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    {
      "start": 441.42,
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      "text": "拿了满分10分",
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    },
    {
      "start": 442.64,
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      "text": "这让他机身同一梯队",
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    {
      "start": 444.3,
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      "text": "与Cloud 3.5",
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    },
    {
      "start": 446.16,
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      "text": "Timmy Chat",
      "chunk": 0,
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    },
    {
      "start": 446.8,
      "end": 448.06,
      "text": "Queen Max",
      "chunk": 0,
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    },
    {
      "start": 448.06,
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      "text": "以及Opus模型并列",
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    {
      "start": 449.4,
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      "text": "有趣的是",
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    {
      "start": 450.06,
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      "text": "在我测试时",
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    },
    {
      "start": 451.6,
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      "text": "V4 Pro预览版也做对了这道题",
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    },
    {
      "start": 453.76,
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      "text": "所以V4系列的推理能力显然非常过瘾",
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    {
      "start": 457.32,
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      "text": "第七题是一个复杂的长城任务",
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    {
      "start": 460.6,
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      "text": "生成一个关于熊猫知识的数据集",
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      "no_speech_prob": 1.576908395528598e-11,
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      "start": 463.58,
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      "text": "并用该数据集为调一个GEM2B模型",
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      "no_speech_prob": 1.576908395528598e-11,
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    {
      "start": 466.88,
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      "text": "接着构建一个本地WebUI每次刷新页面都会生成一条熊猫冷知识",
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      "no_speech_prob": 1.576908395528598e-11,
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    {
      "start": 471.24,
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      "text": "全程从零开始完全自主完成",
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      "start": 473.5,
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      "text": "且完全在本地运行",
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      "start": 475.18,
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      "text": "我们启动任务看看效果",
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      "start": 476.5,
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      "text": "这次的表现绝对可以达满分",
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      "start": 478.56,
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      "text": "它生成了数据集并完成了整个微调",
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    {
      "start": 481.04,
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      "text": "独自运行期间完全没有卡科",
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      "start": 483.672,
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      "text": "生成的Web UI正常运行",
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      "start": 485.532,
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      "text": "每次刷新都显示新的熊猫冷知识",
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    {
      "start": 488.052,
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      "text": "这正是那种复杂的长程智能体任务",
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      "start": 491.372,
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      "text": "Deep C声称对其进行过针对性升级",
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      "start": 493.392,
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      "text": "说实话",
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    {
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      "text": "效果显而易见",
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      "start": 495.432,
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      "text": "之前的预览版远没这么可靠",
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      "start": 497.192,
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      "text": "这波表现确实堪称完美",
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      "text": "第八题也是最后一题",
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    {
      "start": 501.032,
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      "text": "是制作一个3D万秒",
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      "start": 502.112,
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      "text": "要求是一个功能完整的3D万秒",
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    {
      "start": 503.992,
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      "text": "能显示当前实际时间",
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    {
      "start": 505.392,
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      "text": "秒针 分针",
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    {
      "start": 506.312,
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      "text": "以及时针都要平滑转动",
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    {
      "start": 507.832,
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      "text": "还要有日期",
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      "start": 508.492,
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      "text": "核心器显示",
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      "start": 509.492,
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      "text": "以及双时区",
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    {
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      "text": "这是整个测试集中最难的一题",
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      "start": 511.632,
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      "text": "之前没有任何模型",
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    {
      "start": 513.172,
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      "text": "能在这道题上拿到好成绩",
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      "no_speech_prob": 3.143774476854766e-11,
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    {
      "start": 514.572,
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      "text": "历史最高分是Fibo5创下的",
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    {
      "start": 516.712,
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      "text": "4分",
      "chunk": 1,
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      "no_speech_prob": 3.143774476854766e-11,
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    {
      "start": 517.452,
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      "text": "让我们直接运行测试",
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      "no_speech_prob": 3.143774476854766e-11,
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    {
      "start": 518.612,
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      "text": "但你感谢",
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      "no_speech_prob": 3.143774476854766e-11,
      "compression_ratio": 1.3064516129032258,
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    {
      "start": 519.672,
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      "text": "Lip6 V4 Flav竟然拿到了6分",
      "chunk": 1,
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      "no_speech_prob": 3.143774476854766e-11,
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    },
    {
      "start": 521.572,
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      "text": "创下这道题历史记录",
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      "no_speech_prob": 3.143774476854766e-11,
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    {
      "start": 523.212,
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      "text": "手表完整呈现出来",
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    {
      "start": 527.192,
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      "text": "指针转动平滑",
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    {
      "start": 528.272,
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      "text": "只能实时同步时间",
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      "no_speech_prob": 1.709903052482531e-11,
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    },
    {
      "start": 529.552,
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      "text": "双时区设置也基本正常",
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    {
      "start": 531.352,
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      "text": "虽然还不完美",
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    {
      "start": 532.172,
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      "text": "所以没能拿到更高的分数",
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      "start": 534.152,
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      "text": "但这个小模型刚刚击败了",
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      "start": 536.112,
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      "text": "Cloud 3.5 Opus",
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    {
      "start": 537.092,
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      "text": "以及我测试集最难题目上的",
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    {
      "start": 539.272,
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      "text": "其他所有前沿模型",
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    {
      "start": 540.332,
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      "text": "这完全出乎我的意料",
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    {
      "start": 542.092,
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      "text": "来看一下最终的图表",
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      "text": "Deep Seek V4 Flash 0731",
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      "text": "最终拿到了58分",
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      "text": "得分率为72.5%",
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      "text": "这让它排在",
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      "text": "略高于GPT4O513的71.25%",
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    {
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      "text": "稍低于Gemini 1.5 Pro的75%",
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      "start": 558.852,
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      "text": "而在它之上",
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    {
      "start": 559.752,
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      "text": "有Kinning和",
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      "text": "Opas并列77.5%",
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      "text": "GTT 450808达80%",
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      "start": 565.192,
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      "text": "接着是Quain",
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      "text": "Quain 2.5 Max拿下81.25%",
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      "text": "而Cloud 3.5仍以82.5%稳居榜首",
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      "text": "而对比以下背景",
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      "text": "你就能明白这有多惊人",
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      "text": "我之前测试Deep Seek V4 Pro预览版时",
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      "text": "它在此测试中仅得了24.8%",
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      "text": "新的Flash模型得分达到了72.5%",
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      "text": "这意味着该系列的小模型现在",
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      "text": "比大模型预览版的表现提升了近三倍",
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      "text": "在我的测试中",
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      "text": "而且在架构和尺寸与预览版相同",
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    {
      "start": 592.572,
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      "text": "单靠后训练就能取得惊人提升",
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      "text": "不可思议",
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      "text": "这也印证了Deep Seed所宣称的Agent能力升级",
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      "text": "所有射击推理",
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    {
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      "text": "或长城Agent任务基本上都拿到了满分",
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      "text": "比如数学和微调任务",
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      "text": "还有创纪录的时钟生成常识",
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      "start": 611.532,
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      "text": "但像熊猫SVG这种纯视觉打磨的任务",
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      "text": "还有电梯动画",
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      "text": "在这些方面它的表现依然平平",
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    {
      "start": 616.892,
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      "text": "所以这确实很像是一个",
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    {
      "start": 618.592,
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      "text": "针对智能体编程深度微调的模型",
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    {
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      "text": "正如官方基准测试所示",
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      "text": "那么结论是什么",
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      "text": "这绝对是目前性价比最高的模型之一",
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      "text": "可用于智能体编程",
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