搜索“DeepSeek”

new application domain was pioneered by proprietary models — models whose weights are not available under an Open Source Initiative-approved license, like MIT or Apache. But since DeepSeek’s epoch-making launch of DeepSeek-R1, open models have not been far behind. Now, GLM-5 matches the performance of proprietary models released in the last month and is available under the MIT license. For details on the above benchmarks and more, see Z.ai’s blog post. How do I run GLM-5? We’ve written in depth

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ini 3 Pro on key reasoning benchmarks. The table below presents a more comprehensive set of evaluation scores. Capability Benchmark GPT-5.2 -Thinking Claude-Opus -4.5 Gemini 3 Pro DeepSeek V3.2 Qwen3-Max -Thinking Knowledge MMLU-Pro 87.4 89.5 89.8 85.0 85.7 MMLU-Redux 95.0 95.6 95.9 94.5 92.8 C-Eval 90.5 92.2 93.4 92.9 93.7 STEM GPQA 92.4 87.0 91.9 82.4 87.4 HLE 1 35.5 30.8 37.5 25.1 30.2 Reasoning LiveCodeBench v6 87.7 84.8 90.7 80.8 85.9 HMMT Feb 25 99.4 - 97.5 92.5 98.0 HMMT Nov 25 - - 93.3 9

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low latency in demanding production environments. 1. Shared Experts Fusion SGLang PR 13873: Shared Experts Fusion Full credit for this optimization belongs to the original work on Deepseek model. As illustrated in the figure above, MoE models such as GLM4.7 route all input tokens through a shared expert, while each token is also individually routed to its own set of top‑k routed experts as selected by the model’s router. The outputs from all experts are then weighted and aggregated. GLM4.7, for

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rXiv, 2024. [2] Ahmadian, Arash, et al. "Back to basics: Revisiting reinforce style optimization for learning from human feedback in llms." arXiv, 2024. [3] Shao, Zhihong, et al. "Deepseekmath: Pushing the limits of mathematical reasoning in open language models." arXiv, 2024. [4] Yu, Qiying, et al. "Dapo: An open-source llm reinforcement learning system at scale." arXiv, 2025. [5] Liu, Zichen, et al. "Understanding r1-zero-like training: A critical perspective." arXiv, 2025. [6] Yao, Feng, et a

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伯克利函数调用排行榜（该榜单按模型将自然语言请求转化为可执行代码和系统命令的能力进行排名），Anthropic 的 Claude 4 模型目前在工具调用方面表现最佳。 但较新的开源模型正在快速追赶。Goose 的文档重点介绍了几个具有强大工具调用支持的选项：Meta 的 Llama 系列、阿里巴巴的 Qwen 模型、Google 的 Gemma 变体，以及 DeepSeek 以推理为核心的架构。 该工具还与模型上下文协议（Model Context Protocol，简称 MCP）集成，这是一种将 AI 智能体连接到外部服务的新兴标准。通过 MCP，Goose 可以访问数据库、搜索引擎、文件系统和第三方 API——将其能力远远扩展到基础语言模型所能提供的范围之外。 对于有兴趣搭建完全免费、保护隐私的配置的开发者来说，这个过程涉及三个主要组件：Goose 本身、Ollama（一款用于在本地运行开源模型的工具）以及一个兼容的语言模型。 第 1 步：安装 Ollama Ollama 是一个开源项目，它极大地简化了在个人硬件上运行大型语言模型的过程。它通过简单的界面处理下载、优化和提供模型服务

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EXAONE 4.0 32B model, and the open-weight models demonstrating the best performance in their class: gpt-oss-120b (hereafter GPT), Qwen3-235B-A22B-Thinking-2507 (hereafter Qwen3), DeepSeek-V3.2 (hereafter DeepSeek). K-EXAONE demonstrated outstanding competitiveness across diverse fields including knowledge, mathematics, and coding. It particularly achieved excellent results on the knowledge benchmark MMLU-Pro, proving its academic knowledge comprehension and reasoning abilities, and outperformed

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e models we do ablate in the end are all run through OpenRouter, with defaults set to z-ai for GLM 4.6 and GLM 4.5 Air, and nebius/fp8 for INTELLECT-3. We additionally tried using DeepSeek-v3.2 from google-vertex, but the model kept using the wrong function calling format, which invalidated our results. We also tried to ablate Xiaomi's Mimo-v2-flash, but the rate limits were too strict to get meaningful results. It is very important to note that this is not meant to find out any model's absolute

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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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firmly among models up to five times its size while being capable of running locally on consumer hardware. Devstral 2 (123B) and Devstral Small 2 (24B) are 5x and 28x smaller than DeepSeek V3.2, and 8x and 41x smaller than Kimi K2—proving that compact models can match or exceed the performance of much larger competitors. Their reduced size makes deployment practical on limited hardware, lowering barriers for developers, small businesses, and hobbyists.hardware. Devstral 2 supports exploring code

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of SoTA Qwen3 thinking models under high pass@k settings even prior to explicit post-training for reasoning, and exceeds other strong reasoning models such as Phi4-reasoning, and Deepseek-R1-Distill. Building on our previous work with AMD, we have been deeply committed to de-risking end-to-end large-scale pretraining on AMD. To create the infrastructure required to train ZAYA1-base, Zyphra, AMD, and IBM collaborated closely to build a large-scale, reliable, and highly performant training cluste

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er efficiency, scalability, and reasoning performance than traditional dense architectures. This paradigm shift defines today’s leading frontier models including GPT-5, Claude-4.5 DeepSeek-V3 and Kimi2 all of which leverage MoE designs to expand capability while optimizing compute utilization. ZAYA1 represents the first large-scale pretraining of an MoE model on an AMD platform, demonstrating that the AMD AI ecosystem is ready to power frontier-class AI development end-to-end. Zyphra co-designed

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wise training-inference consistency a scalable and reusable feature. If you're interested or would like to contribute, please join these Slack channels: sig-post-training; sig-batch-invariant Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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From DeepSeek R1 to MiniMax-M2, the largest and most capable open-weight LLMs today remain autoregressive decoder-style transformers, which are built on flavors of the original multi-head attention mechanism. However, we have also seen alternatives to standard LLMs popping up in recent years, from text diffusion models to the most recent linear attention hybrid architectures. Some of them are geared towards better efficiency, and others, like code world models, aim to improve modeling performanc

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cial thanks to Vensen Mu, Jeff Aw, Jun Kang Chow, Tun Jian Tan, Pin Siang Tan, Amir Balwel, Ye Hur Cheong, Zhiyao Cen and Kaichao You for developing the Sleep Mode feature and this blog post. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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Lightning is an open-source project from Microsoft Research. We sincerely appreciate the support from MSR for this open-source exploration. Yuge Zhang is the primary contributor to this work. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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的其他内容。你基本上有九个左右的选择，因为只有少数几家公司能制造前沿模型。它们都提供一定的免费访问。最先进的四套 AI 系统是 Anthropic 的 Claude、Google 的 Gemini 、OpenAI 的 ChatGPT ，以及 Elon Musk 的 xAI 旗下的 Grok。然后是开放权重 AI 家族，它们几乎（但并非完全）同样出色：中国的 Deepseek 、Kimi 、Z 和 Qwen，以及法国的 Mistral。在几乎任何 AI 评级系统中，这些 AI 模型的变体合计占据了前 35 名。你使用的任何其他提供前沿 AI 的服务，从 Microsoft Copilot 到 Perplexity（两者都提供一定的免费使用），都是以这九种 AI 中的一个或多个为基础驱动的。 你应该如何在它们之间做选择？一些免费系统（如 Gemini 和 Perplexity）擅长网络搜索，而另一些则完全无法搜索网络。如果你想要免费的图像生成，最佳选择是 Gemini，ChatGPT 和 Grok 紧随其后。但归根结底，这些 AI 在许多细微之处存在差异，包括隐私政策、访问级别、能力、它们

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is ongoing and there are an overwhelming number of improvements on the horizon. Looking ahead, we’re working to unlock significant throughput gains on cluster-scale inference for DeepSeek, Qwen, gpt-oss, and many more through speculative decoding and Data+Expert Parallel (DEP) configurations. With NVIDIA’s gpt-oss-120b-Eagle3-v2, which incorporates Eagle speculative decoding, we anticipate up to 2-3x improvement in throughput. Leveraging DEP, which takes advantage of the 1,800 GB/s low latency

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egorize PRMs as “step-level judges,” which are predominantly developed for training, not pure evaluation. (In practice, PRMs are difficult to train reliably at scale. For example, DeepSeek R1 did not adopt PRMs and instead combined verifiers for the reasoning training.) Judge-based evaluations offer advantages over preference-based leaderboards, including scalability and consistency, as they do not rely on large pools of human voters. (Technically, it is possible to outsource the preference-base

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Wonkee Lee 2025.10.02 Recently, with the rise of high-performance models such as LG's EXAONE-4.0, OpenAI’s GPT-o series (o1, o3)[1], DeepSeek-R1[2], the evolution of LLMs has rapidly shifted from instruction-following models, which tend to provide straightforward answers, toward agents capable of deep reasoning. These models are commonly referred to as Large Reasoning Models (LRMs) and are distinguished from traditional instruction models by their ability to perform multi-step reasoning to addre

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ities and are able to have multi-turn interactions with the user and tools to get to a final result. qwen3; gpt-oss Recommended cloud models: qwen3:480b-cloud; gpt-oss:120b-cloud; deepseek-v3.1-cloud The web search and web fetch tools can return thousands of tokens. It is recommended to increase the context length of the model to 32000 tokens for reasonable performance. Search agents work best with full context length. Fetching page results To fetch individual pages (e.g. when a user provides a

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