搜索“DeepSeek”

cutives cited model cost as the primary obstacle to generative AI adoption. Chinese labs have shipped some of the most aggressively priced, yet competitive open models. The latest DeepSeek-V4-Flash costs just 1/105 of Anthropic’s Fable 5 on one task, according to Artificial Analysis. Even at that ratio, DeepSeek’s CEO said in a investor meeting that the company still runs a 60% profit margin and can recoup a model’s cost within 10 months. A growing trend today is developers now route models by c

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mbership, an enterprise tier bundling its Allegretto plan benefits with corporate billing and support. (@Kimi Moonshot) A UK/US study found Kimi K3 significantly lags American models in offensive cyber “hacking power”—a data point that cuts against the panic narrative above (SCMP); Recode China AI published a full translation of DeepSeek founder Liang Wenfeng’s four-hour investor meeting—his AGI roadmap, the US-China compute gap, Huawei chips, and why DeepSeek stays open source (Recode China AI)

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models and is okay for working with office documents but lags badly in terms of its agentic abilities. And for the technically inclined, Chinese open weights models like Kimi K3, DeepSeek, and Qwen are surprisingly capable, but do require expertise to use as agents. And then there is Google. Google, which led on benchmarks not that long ago, has fallen behind where it now counts: it has no leading frontier model and it has nothing close to Codex and Code. That is why I don’t suggest Gemini as y

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to something shorter, say 3-5 months. From the release materials, it is clear that K3 is a true frontier model. It will be the closest open models have been to the frontier since DeepSeek R1 . DeepSeek R1 was a different story. This was a Chinese lab being extremely quick to pivot to reasoning models and release one faster than many American companies. Kimi K3 an example of a Chinese lab executing on scaling the known areas: data, algorithms, architecture, tools, environments, etc. Kimi K3 come

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核心的经济代言人来代表针对它们的行动可能带来的负面影响。从近期围绕Fable（以及随后的GPT-5.6）模型许可协议相关事件的报道来看，关于6月9日发生了什么，有更多说法： 据一位了解该会议的人士称，会上提到了该项目将如何处理开源AI模型的话题。该人士说，美国开源模型提供商Reflection AI的一位代表主张，开源模型应基于其能力获得该框架的豁免。目前，DeepSeek等中国开源模型相比其他可用的开放模型拥有相当大的领先优势，而Reflection尚未推出公开模型。 在这里实施任何形式的禁令，对AI的长期发展轨迹都将是一个重大错误。 最可能即将采取的行动是，禁止或无限期推迟任何能力明显高于GPT 5.5、Claude Opus 4.8或GLM-5.2区间的开放权重模型。鉴于持续存在的能力差距，这应该会在未来6个月内发生。 就目前情况而言，这些模型最有可能来自一家中国公司，正因如此，这场关于前沿开放模型能力的讨论不可避免地与其他问题（如蒸馏）联系在一起。政府“审查权”的能力门槛会随时间推移而变化，但一旦确立，对开放模型的推进很可能远慢于对闭源模型。部分原因是闭源模型更容易保障安全，但

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a systems, it’s exploring all possible logical consequences of any kind of axiom. Dwarkesh Patel On the point about whether you can provide process-based supervision without Lean, DeepSeek had their DeepSeek Math model. They released a paper on how they trained it, and it was quite interesting. The problem with natural language proofs is you don’t know if it’s correct or not. They have a verifier, and the verifier is trained by a meta-verifier that makes sure that for all the problems they’re tr

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es: “Pure” model makers : These are companies whose stated goal is to train models that are at the frontier, or at least close to it. This includes many Chinese companies, such as DeepSeek, Zhipu, and Minimax, but also Western ones like Poolside, Arcee, and Zyphra. It also increasingly includes sovereign AI players, such as Cohere, Sovereign, Mistral, and Trillion Labs. The recent Mythos episode has woken up some policymakers, which may lead to increased interest in sovereign model training. Big

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之一，但那是另一个话题了。有一个在社区中评价褒贬不一的基准测试（尤其是在真正的设计师群体中）——Design Arena 甚至显示 GLM-5.2 击败了 Claude Fable 本身——那个最近被封禁的炒作机器！ 在 AI 评论界和研究界中，我尊重的几乎所有人都在亲自使用后称赞了这款模型。社区中如此集中的讨论焦点，此前只有一次在开源模型发布时如此明显——DeepSeek R1。这不是我轻易做出的比较，而当我将 Kimi K2 的发布比作“DeepSeek 时刻”时，GLM-5.2 已经远远超过了那一次。Kimi K2 令人印象深刻之处在于，开源模型性能的大幅进步似乎可以来自中国的任何地方。而 GLM-5.2 所迈出的这一步，更像是 AI 进步的一扇单向门。 Anthropic 凭借 Claude Code 创下的创纪录营收增速，很大程度上是由其作为最佳模型、以及唯一真正能做到这一点的模型所驱动的。GLM-5.2 是众多（即将到来的）开源权重模型中第一个提供可信替代方案的。这一类比非常清晰，就像当年 DeepSeek R1 表明，资源远为匮乏的开源权重实验室也能复现 OpenAI 以

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e can’t easily model progress over time. On the other hand, we can look at some of the research being done to get a feel for the progress. Some of the types of work include: Using DeepSeek’s models to try to build better GPU kernels ( 400 ), automating the conversion of PyTorch modules to CUDA code ( 401 ), Meta using LLMs to automate the generation of optimized Triton kernels for use within its infrastructure ( 439 ), using LLMs to help write kernels for non-standard hardware like Huawei’s Asce

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o do one thing at enormous scale, and that thing is becoming less differentiated over time. For instance, Kimi K2.5, Minimax 2.5 and GLM-5 were all released in the last week, with Deepseek-V4 and Qwen-3.5 on the horizon. The specialisation game, in contrast, requires doing a thousand things at small scale, and each of those things creates compounding value that’s hard to replicate. OpenAI offering an API fine-tuning endpoint is to genuine specialisation what a hotel concierge recommending a rest

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till worked, but it didn’t really change how LLMs behaved or felt in practice (the only exception to that was OpenAI’s freshly released o1, which added reasoning traces). So, when DeepSeek released their R1 paper in January 2025, which showed that reasoning-like behavior can be developed with reinforcement learning, it was a really big deal. (Reasoning, in the context of LLMs, means that the model explains its answer, and this explanation itself often leads to improved answer accuracy.) 1.1 The

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h is approximately 1Tbit/s of unidirectional bandwidth vs 14.4Tbit/s of NVLink for R200. This is enough I/O for performing prefill even for modern MoE frontier LLMs. For instance, DeepSeek V3 when running on the NVFP4 number format will require 335GB of memory capacity to load all the model weights – this exceeds the 128GB of memory capacity of a single CPX chip. This can be overcome by using pipeline parallelism (‘PP’), where multiple layers of a model are split across different GPUs. In PP, ea

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But constraints have also led to adaptation, and Chinese companies have adapted. High batch sizes and disaggregated serving are but two examples. Despite advances, in the case of DeepSeek, most of their tokens are still inferenced on western hardware. We wrote about this dynamic in our recent DeepSeek debrief. Training of DeepSeek's next generation model was also delayed by the use of Huawei chips, as we also said in the debrief. This is not a stable equilibrium. There are always moving pieces

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from 128 H100s to 2048 H100s and across different versions of Nvidia software. Later in this report, we will also analyze GB200 NVL72 benchmark results across Llama4 400B MoE and DeepSeek 670B MoE and compare this data to our earlier results from the H100. We will discuss whether the GB200 NVL72 performance per $ advantages survives once reliability issues are factored in. Downtime from poor reliability and lost engineering time is one of the main factors that we will capture in our perf per TC

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spite seemingly unlimited resources, Meta has been falling behind foundation labs in model performance. The real wake-up call came when Meta lost its lead in open-weight models to DeepSeek. That stirred the sleeping giant. Now in full Founder Mode, Mark Zuckerberg is personally leading Meta’s charge, identifying Meta’s two core shortcomings: Talent and Compute. As one of the last founders still running a tech behemoth, Mark doesn’t need SemiAnalysis to tell him to slow down stock buybacks to fun

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