同时，Cognition 称，得益于与推理服务提供商 Cerebras 的合作，SWE-1.5 的运行速度最高可达 Anthropic 旗下 Sonnet 4.5 模型的 13 倍。 比 Sonnet 4.5 快 13 倍，编码性能近 SOTA "开发者不应在'思考速度快'与'思考质量高'的人工智能之间做选择。"Cognition 在官方声明中表示，这一理念是 SWE-1.5 的设计基础。 据介绍，SWE-1.5 经过专门设计，是一款拥有数千亿参数的前沿规模模型，旨在打破上述权衡困境的同时提供顶尖性能与一流速度。而该模型最显著的特 点是其原始速度，这一优势源于与推理领域专业机构 Cerebras 的深度合作：共同部署并优化 SWE-1.5。具体举措包括训练一个经过优化的草稿模型以实现 更快的投机解码以及构建定制化请求优先级系统，让端到端智能体交互过程更流畅。 Cognition 表示，此次合作让 SWE-1.5 实现了极佳的延迟表现，并"还树立了新的速度标准"，使其处理速度最高可达 950 token / 秒，分别是 Haiku 4.5 模型 的 6 倍、Sonnet 4.5 模型的 13 倍。"这一性能飞跃 ...

Core Insights - Cognition has launched its new high-speed AI coding model SWE-1.5, designed for high performance and speed in software engineering tasks, now available in the Windsurf code editor [2][3] - SWE-1.5 operates at a speed of up to 950 tokens per second, making it 13 times faster than Anthropic's Sonnet 4.5 model, and significantly improving task completion times [3][4][6] Performance and Features - SWE-1.5 is built on a model with hundreds of billions of parameters, aiming to provide top-tier performance without compromising speed [3][4] - The model's speed advantage is attributed to a collaboration with Cerebras, which optimized the model for better latency and performance [3][6] - In the SWE-Bench Pro benchmark, SWE-1.5 achieved a score of 40.08%, just behind Sonnet 4.5's 43.60%, indicating near-state-of-the-art coding performance [6] Development and Infrastructure - SWE-1.5 is trained on an advanced cluster of thousands of NVIDIA GB200 NVL72 chips, which offer up to 30 times better performance and 25% lower costs compared to previous models [10] - The training process utilizes a custom Cascade AI framework and incorporates extensive reinforcement learning techniques to enhance model capabilities [10][11] Strategic Vision - The development of SWE-1.5 is part of a broader strategy to integrate AI coding capabilities directly into the Windsurf IDE, enhancing user experience and performance [13][15] - Cognition emphasizes the importance of a collaborative system that includes the model, inference process, and agent framework to achieve high speed and intelligence [13][14] Market Position and Competition - The launch of SWE-1.5 coincides with Cursor's release of its own high-speed model, Composer, indicating a strategic convergence in the AI developer tools market [17] - Both companies are leveraging reinforcement learning in their models, highlighting a shared approach to creating efficient coding agents [17] User Feedback and Performance - Early user feedback on SWE-1.5 indicates a perception of high speed, although some users reported issues with task completion compared to other models like GPT-5 [18][19]

https://mp.weixin.qq.com/s/_dmZj9IwtbRLpvXHulQ_8g 二、Cursor 2.0更新,自研模型Composer,多agent并行 生成式AI 一、OpenAI 刚刚开源了两个专门用于安全分类的推理模型 1. OpenAI开源gpt-oss-safeguard安全分类模型(120b和20b版本),采用Apache 2.0许可证,能直接理解策略文档进 行内容分类无需重新训练； 2. 该模型在多个基准测试中表现超越GPT-5-thinking,在内容审核评估集和ToxicChat数据集上达到行业最佳性价 比； 3. OpenAI内部已使用该技术(Safety Reasoner原型)处理图像生成和Sora 2等产品,安全推理算力占比高达16%。 1. Cursor发布2.0版本,推出首个自研编码模型Composer,生成速度达每秒250个token,是同类前沿系统的4倍,标志 从"AI外壳"向"AI原生平台"转型； 2. Composer采用混合专家(MoE)架构,通过强化学习针对软件工程优化,在Cursor Bench评测中达到前沿水平,已被团 队日常开发使用； 3. 新 ...

Cursor迎来重大升级，2.0版本来了！ 一直「套壳」的Cursor这次终于发布了首款自研编码模型Composer。 Composer的速度是同等模型的4倍。 Cursor说这是一款专门为低延迟智能编码打造的模型，大部分任务都可以在30秒以内完成。 在Speed一栏，Composer的速度达到了200 Tokens/秒。 【导读】这次不仅发布自研编码模型Composer，还重构了IDE交互逻辑，可以最多8个智能体同时跑，早期测试和开发者都说Cursor 2.0真的太快了。 这次2.0版本还将浏览器嵌入编辑器内，这对于前端开发非常友好。 可以直接选择元素并将DOM信息转发给Cursor。 可以看这个实测，前端开发可以直接在浏览器选定元素，Cursor自动识别对应代码。 这次更新还引入了全新的代码审查功能，更容易查看 Agent 在多个文件中的所有更改，无需在各个文件之间来回切换。 这次一个更大的更新是引入了语音模式（Voice Mode），真就是动嘴编程了。 除了自研模型，Cursor这次重构了交互逻辑，带来了多智能体模式，在单个提示下，可最多并行运行8个智能体。此功能使用git worktrees或远程机器 ...

智东西10月30日报道，今天，知名AI编程平台Cursor宣布升级到2.0版本，并推出了Cursor首个自研编程模型Composer，以及用于并行协作多 个Agent的新界面等15项升级。 Composer模型最大的特点就是快。Cursor称，该模型专为在Cursor中进行低延迟的Agentic编程而打造，大多数回合在30秒内即可完成，其速度 达到同等智能模型的4倍，每秒输出的token数已经超过200个。 在Cursor的内部评估中，Composer的智能水平已经超过了最佳的开源编程模型（包括Qwen Coder和GLM 4.6），速度则优于现有的前沿轻量 级模型（包括Claude Haiku 4.5和Gemini Flash 2.5），不过，其智能水平仍然低于GPT-5和Claude Sonnet 4.5。 ▲Composer与前沿开源、闭源模型的智能与速度对比 随着模型Agent能力的不断提升，Cursor的UI也随之升级。Cursor 2.0的UI界面不再以文件为核心，而是围绕Agent进行重新设计，开发者可以 聚焦想要的目标，让不同的Agent分别处理实现细节。 Cursor 2.0现已支持并行运行 ...

机器之心报道 机器之心编辑部 终于，Cursor 还是走上了自己训练大型语言模型的路。 Cursor 2.0 终于来了！ 刚刚，Cursor 发布了两项重大更新：首个编码模型 Composer，以及用于并行协作多个智能体的新界面。 此举意义非凡。一直以来，Cursor 虽然广受欢迎，但终究免不了「AI 时代的 VS Code」的帽子，因为它此前只能使用 Claude、GPT 等第三方模型。这既是 Cursor 的起点，也成了它的瓶颈。 Composer 的发布，堪称是 Cursor 打破这个瓶颈的「独立宣言」，这也标志着 Cursor 正式从「AI 外壳」向「AI 原生平台」进化。 自研模型 Composer Composer 是一款前沿模型，虽然智能程度不敌 GPT-5 等最佳前沿模型，但速度确实遥遥领先，达到了同等智能模型的 4 倍。 在基准测试中， Composer 实现了前沿水平的编码智能，同时生成速度达到每秒 250 个 token—— 大约是领先的快速推理模型的两倍，是同类前沿系统的四倍。（注： Cursor 发布的对比将模型分为几个类 别：「最佳开源」（例如， Qwen Coder 、 GLM ...

Core Insights - Cursor has officially released its first in-house coding model, named Composer, as part of the Cursor 2.0 update [1][2] - Composer is reported to complete complex tasks in just 30 seconds, achieving a speed increase of 400% compared to competitors [3][12] Model Features - The new Cursor 2.0 includes a native browser tool that allows the model to test, debug, and iterate code autonomously until achieving correct results [4] - Voice code generation enables users to convert their thoughts into code without typing [5] - The interface has shifted from a file-centric to an agent-centric model, allowing multiple agents to run simultaneously without interference [6][7] Performance Metrics - Composer generates code at a speed of 250 tokens per second, which is approximately twice as fast as the current leading models like GPT-5 and Claude Sonnet 4.5 [19][20] - The model demonstrates enhanced reasoning and task generalization capabilities, comparable to mid-tier leading models [21] Training Methodology - Composer's performance is attributed to reinforcement learning, which allows the model to learn from real programming tasks rather than static datasets [22][26] - The training process involves the model working directly within a complete codebase, utilizing production-level tools to write, test, and debug code [27][28] Practical Application - Cursor 2.0 is designed to provide a practical AI system that aligns closely with developers' daily workflows, enhancing its usability in real-world scenarios [35][36] - The model has shown emergent behaviors, such as running unit tests and autonomously fixing code format errors [31] Transparency and Model Origin - There are concerns regarding the transparency of Composer's foundational model, with questions about whether it is based on pre-existing models or entirely self-trained [37][40] - Cursor has previously developed an internal model named Cheetah, which was used for testing speed and system integration [42]