[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-deepseek-v4-flash-non-reasoning-moe-7x-cheaper":3,"topics-all":36,"news-related-630c8170-7355-46b8-bd74-32215ece8556":55},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"630c8170-7355-46b8-bd74-32215ece8556","DeepSeek V4 Flash（非推理版）：开源 MoE 的低价高吞吐新选项","5月12日，DeepSeek 悄然上线了 DeepSeek-V4-Flash 非推理版，这是继4月24日 V4 系列发布后的又一次版本补充。与此前发布的 V4-Pro 和带推理能力的 V4-Flash 不同，这次的非推理版本彻底放弃了思维链推理能力，换来的是更低的 API 价格和更高的吞吐量——输入 tokens 成本仅为 V4-Pro 的约1\u002F7，每百万 tokens 仅0.14美元，输出也只要0.28美元。\n\n从架构上看，V4-Flash（非推理）仍然保留了稀疏 MoE 的核心设计：284B 总参数量，每次推理激活13B参数，上下文窗口达1M tokens。官方透露，在简单 Agent 任务上，其表现与 V4-Pro 持平。这并不令人意外——当任务不需要多步推理时，强大的思维链能力就成了多余的功耗。\n\n这次发布的战略意图相当明确：在不需要复杂推理的消费级应用场景（批量文案处理、客服对话、文档摘要等），DeepSeek 希望以价格优势抢占市场份额。V4-Flash（非推理）每百万 tokens 输入仅需0.14美元，这个价格在开源 MoE 模型中极具竞争力，甚至可以与不少闭源小模型贴身肉搏。\n\n从行业角度看，这种去掉推理能力换低价的策略折射出一个趋势：2026年的 LLM 市场正在加速分层。高端推理任务由 GPT-5.5、Claude Opus 4.7 等顶级闭源模型把持，而在中端场景，开源 MoE 模型正在快速填补空白，并通过极致压缩成本来争夺本属于传统 NLP 管道的市场。\n\n值得关注的是，DeepSeek 选择在 V4-Pro 正式发布不到三周后就推出非推理变体，说明其产品迭代节奏已经比传统闭源厂商快出一个量级。对开发者而言，这既是好事——有更多工具箱可选；也是挑战——模型选择的技术门槛正在上升，选错型号可能意味着浪费算力或达不到效果。\n\n未来，随着更多去推理化的变体出现，我们或许会看到 LLM 的定价体系进一步碎片化：同系列模型的不同配置，将覆盖从几美元到几厘每 token 的广泛价格带。DeepSeek V4-Flash（非推理）只是这场效率战争的最新一枚棋子。","https:\u002F\u002Fcrafiq.ai\u002Fmodels\u002Flanguage\u002Fdeepseek-deepseek-v4-flash-non-reasoning","11d9bda8-057a-48b3-a819-912f1ab5094f",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"776b83de-6b7b-4a74-85a2-56255d40a007","en","DeepSeek V4 Flash non-reasoning: cheap, high-throughput MoE","On May 12, DeepSeek quietly launched DeepSeek-V4-Flash (Non-Reasoning), another version addition following the V4 series release on April 24. Unlike the previously released V4-Pro and the reasoning-capable V4-Flash, this non-reasoning version completely abandons chain-of-thought reasoning capability, in exchange for lower API pricing and higher throughput — input token cost is only about 1\u002F7 of V4-Pro, at just $0.14 per million tokens, and output is also only $0.28.\n\nArchitecturally, V4-Flash (Non-Reasoning) still retains the core sparse-MoE design: 284B total parameters, with 13B activated per inference, and a 1M-token context window. Officially disclosed, on simple agent tasks, its performance is on par with V4-Pro. This isn't surprising — when the task doesn't require multi-step reasoning, powerful chain-of-thought capability becomes unnecessary power draw.\n\nThe strategic intent of this release is quite clear: in consumer-grade application scenarios that don't need complex reasoning (batch copywriting processing, customer service dialogue, document summarization, etc.), DeepSeek hopes to grab market share with a price advantage. V4-Flash (Non-Reasoning) at $0.14 per million input tokens is extremely competitive among open-source MoE models, even able to slug it out with many closed-source small models.\n\nFrom an industry perspective, this strategy of dropping reasoning capability for lower price reflects a trend: the 2026 LLM market is accelerating stratification. High-end reasoning tasks are dominated by top closed-source models like GPT-5.5 and Claude Opus 4.7, while in mid-range scenarios, open-source MoE models are rapidly filling the gap, and competing for the market that traditionally belonged to NLP pipelines through extreme cost compression.\n\nWhat's worth attention is that DeepSeek chose to launch the non-reasoning variant less than three weeks after the official V4-Pro release, indicating its product iteration pace is now an order of magnitude faster than traditional closed-source vendors. For developers, this is both a good thing — more tools in the toolbox — and a challenge — the technical bar for model selection is rising, and choosing the wrong model may mean wasted compute or suboptimal results.\n\nIn the future, as more de-reasoned variants appear, we may see further fragmentation in LLM pricing: different configurations of the same model family will cover a wide price band from a few dollars to a fraction of a cent per token. DeepSeek V4-Flash (Non-Reasoning) is just the latest piece on this efficiency-war chessboard.","deepseek-v4-flash-non-reasoning-moe-7x-cheaper","2026-05-13T02:10:00Z","2026-05-13T10:19:03.162583Z","2026-08-19T02:08:40.142862Z",true,"agent",159,[37,46],{"slug":38,"tag_slug":38,"title_zh":39,"title_en":40,"intro_zh":41,"intro_en":42,"id":43,"is_active":33,"created_at":44,"modified_at":45},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":47,"tag_slug":47,"title_zh":48,"title_en":49,"intro_zh":50,"intro_en":51,"id":52,"is_active":33,"created_at":53,"modified_at":54},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":56},[57,62,67,72,77,82],{"id":58,"title":59,"news_slug":60,"published_at":61},"559df680-f13e-48f0-9263-73116aaff411","上交大DLite免历史投机解码:Agent提速2.74倍","dlite-history-free-speculative-decoding","2026-10-19T17:07:52+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"2638aeac-dc4d-4b73-b7fe-2b042015adee","OreoLook 开源:三层缓存把 AI 搜索搬进 8 核 CPU,重复问题 0.1 毫秒出答案","oreolook-three-layer-cpu-cache","2026-09-10T23:08:36+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"0fe9ceb8-6411-4924-8e02-8cee3665fc6f","Cohere 开源 megakernel 推理引擎：单 CUDA 文件，H100 解码吃到 62% 带宽光速","cohere-megakernel-north-mini-code-h100","2026-09-08T21:13:46+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"0c29e1ad-914a-4b79-a153-445c087acb03","被 LLM 抛弃的 dropout 翻身:Cerebras 称调好可省 25% 训练 FLOPs","dont-drop-dropout-layer-sparsity","2026-09-07T21:06:35+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"199cd4ef-f092-45a5-8635-91778dd2bce2","编译即训练：一句规约炼出 83.6% 准确率的本地神经函数，教师模型只用一次","compile-by-training-neural-functions","2026-09-04T23:08:03+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"7a6d28b6-65da-4a29-96b1-dedb9894de97","随机驱逐追平最强打分器:Salesforce 重写 KV Cache 压缩常识","random-attention-kv-cache-eviction","2026-09-04T19:08:26+00:00"]