[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-deepseek-llama4-qwen3-spheron-comparison":3,"topics-all":36,"news-related-3ef2acbe-e09c-4ab6-9104-e8157e729698":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},"3ef2acbe-e09c-4ab6-9104-e8157e729698","开源大模型2026实用评测：DeepSeek V3.2、Llama 4与Qwen3谁更值得部署？","当前（2026年5月）开源大模型战场上，三个家族已经形成清晰的技术分化：DeepSeek V3.2 Speciale（MoE，685B总参，MIT许可）、Meta Llama 4 Scout\u002FMaverick（MoE，109B\u002F400B总参，Llama 4 Community许可）、阿里Qwen3-32B（Dense，32.8B，Apache 2.0许可）。最近Spheron发布的一份实测报告，为这场竞争提供了详尽的成本与性能数据。\n\n硬件门槛差异显著。三家模型的最低部署成本差距悬殊。Qwen3-32B和Llama 4 Scout均可在单张H100 80GB上运行，Spot实例成本约每小时0.87美元\u002F百万Token，而DeepSeek V3.2 Speciale由于采用MoE架构且需要激活37B参数，最低需要8×H100，推理成本骤升至每小时13.89美元\u002F百万Token——是前者的16倍。这意味着如果业务不需要DeepSeek在数学推理上的特长，选择前两者能节省大量算力成本。\n\nBenchmark各有胜负。MMLU通用知识测试中，DeepSeek V3.2 Speciale以88.5%领先，Llama 4 Maverick为85.5%，Qwen3-32B为83.6%。但在代码能力HumanEval上，Qwen3-32B反而以88.0%的成绩反超DeepSeek V3.2 Speciale的82.6%（注：后者测的是HumanEval-Mul多语言版本，两基准不可直接对比）。Llama 4 Scout因评测数据官方未公开，结果存疑。上下文窗口方面，Llama 4 Scout的10M超长上下文是最大亮点，适合RAG和长文档问答场景；DeepSeek V3.2和Qwen3-32B分别在128K和131K级别。\n\n效率革命：MoE与Dense的路线之争。三条路线的效率哲学截然不同。DeepSeek V3.2走高参数+稀疏激活路线，总参685B但每次只激活37B，换来的是超强推理能力，代价是内存占用大；Llama 4 Scout是中等参数+极稀疏激活的典型，109B总参仅激活17B，10M上下文靠的是YaRN长度外推；Qwen3-32B坚守Dense路线，简单直接，33GB INT4量化文件一张H100就能装下，胜在够用就好。\n\n许可证决定了能用在哪里。Qwen3的Apache 2.0最友好，没有任何用户量限制；Llama 4 Community License限制月活超7亿需Meta授权；DeepSeek V3.2的MIT许可同样宽松，但对深度定制场景不如Apache 2.0明确。\n\n选型建议。一个简单框架：代码助手选Qwen3-32B，性价比最高；长文档RAG选Llama 4 Scout，10M上下文无可替代；数学\u002F复杂推理选DeepSeek V3.2 Speciale，但先确认预算能cover 8×H100的部署成本。没有免费午餐，但开源生态给了开发者前所未有的选择自由。","https:\u002F\u002Fwww.spheron.network\u002Fblog\u002Fdeepseek-vs-llama-4-vs-qwen3\u002F","cdf235af-8a3d-482b-a40f-8b0ee27c80bd",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"120fa59a-ff6f-4537-9bf5-f818df636a0e","benchmark",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},"96d14ac2-a6e4-4b2e-b39e-6752eb879a0e","en","2026 open-source showdown: DeepSeek V3.2 vs Llama 4 vs Qwen3","As of May 2026, the open-source large-model battlefield has three families in clear technical differentiation: DeepSeek V3.2 Speciale (MoE, 685B total params, MIT license), Meta Llama 4 Scout\u002FMaverick (MoE, 109B\u002F400B total params, Llama 4 Community license), and Alibaba Qwen3-32B (Dense, 32.8B, Apache 2.0 license). A recent benchmark report from Spheron provides detailed cost and performance data for this competition.\n\n**Hardware thresholds differ significantly.** The minimum deployment costs of the three models are starkly different. Both Qwen3-32B and Llama 4 Scout can run on a single H100 80GB, with Spot pricing around $0.87\u002Fhour per million tokens. DeepSeek V3.2 Speciale, using MoE and activating 37B parameters, needs at least 8×H100, with inference cost jumping to $13.89\u002Fhour per million tokens — 16× the former. This means if your business doesn't need DeepSeek's math-reasoning specialty, choosing the first two saves significant compute cost.\n\n**Benchmarks each have their wins and losses.** In MMLU general knowledge, DeepSeek V3.2 Speciale leads with 88.5%, Llama 4 Maverick is at 85.5%, Qwen3-32B at 83.6%. But in code ability on HumanEval, Qwen3-32B instead takes the lead at 88.0%, surpassing DeepSeek V3.2 Speciale's 82.6% (note: the latter uses the HumanEval-Mul multilingual version, so they're not directly comparable). Llama 4 Scout's official evaluation data is unpublished, results are uncertain. On context window, Llama 4 Scout's 10M ultra-long context is the biggest highlight, suitable for RAG and long-document Q&A; DeepSeek V3.2 and Qwen3-32B are at 128K and 131K respectively.\n\n**Efficiency revolution: MoE vs Dense route debate.** The three routes' efficiency philosophies are radically different. DeepSeek V3.2 takes a high-parameter + sparse-activation route, with 685B total but only 37B activated per pass, trading strong reasoning for high memory usage; Llama 4 Scout is a typical mid-parameter + ultra-sparse case, 109B total with only 17B activated, with 10M context relying on YaRN length extrapolation; Qwen3-32B holds the Dense line, simple and direct, a 33GB INT4 quantized file fits in one H100, winning on \"good enough is enough.\"\n\n**Licenses determine where you can use them.** Qwen3's Apache 2.0 is most friendly, with no user-count restrictions; Llama 4 Community License requires Meta authorization for monthly active users above 700M; DeepSeek V3.2's MIT license is similarly permissive, but less explicit than Apache 2.0 for deep customization scenarios.\n\n**Selection advice.** A simple framework: code assistant pick Qwen3-32B for the best cost-performance; long-document RAG pick Llama 4 Scout for its irreplaceable 10M context; math\u002Fcomplex reasoning pick DeepSeek V3.2 Speciale, but first confirm the budget can cover 8×H100 deployment costs. There's no free lunch, but the open-source ecosystem gives developers unprecedented choice.","deepseek-llama4-qwen3-spheron-comparison","2026-05-19T13:00:00Z","2026-05-19T13:05:42.389175Z","2026-08-19T02:08:40.142862Z",true,"agent",218,[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},"2d866988-f04f-42e8-85e1-8a49069f9222","MaxProof测试时缩放：MiniMax M3拿下IMO 2025\u002F USAMO 2026双金","minimax-m3-maxproof-imo-2025-usamo-gold","2026-06-14T12:00:00+00:00",{"id":63,"title":64,"news_slug":65,"published_at":66},"2fd15c4a-7c8c-42c4-b90b-4ae15396be65","DeepSeek V4 Pro独立评测：开源模型逼近前沿，但能力仍差8个月","caisi-deepseek-v4-pro-8-month-gap-cost-efficient","2026-05-02T07:05:00+00:00",{"id":68,"title":69,"news_slug":70,"published_at":71},"8def771a-d936-4859-930d-02c3011dc55c","LimiX-2 开源：一个模型吃下分类回归插补，表格三榜登顶","limix-2-tabular-foundation-model","2026-09-17T21:09:27+00:00",{"id":73,"title":74,"news_slug":75,"published_at":76},"176b4807-da61-479f-a514-9381cd13319e","SP3O:3 个锚点修复 PPO critic 的平坦化","sp3o-sparse-critic-supervision","2026-09-17T17:10:01+00:00",{"id":78,"title":79,"news_slug":80,"published_at":81},"2e27016d-b90e-45c7-825a-41fd1e435c80","JHU 新研究:组合持续学习机制,百任务记忆留存从 1.2% 提到 34.9%","compose-cl-long-horizon-memorization","2026-09-16T15:10:00+00:00",{"id":83,"title":84,"news_slug":85,"published_at":86},"4c4a2a9e-f69b-4985-bd42-97ab2ef4e2ac","Spark-X2.5-4B 开源:4B 跑 1M 上下文,22 项基准打 9B 级 Qwen3.5","spark-x2-5-4b-apache-open-source","2026-09-16T01:30:00+00:00"]