搜索“Anthropic”

Claude Haiku 5.5 (High) by @AnthropicAI has landed in the Agent Arena! Across +20.5K real-world agentic sessions from our global community of users, this release scored +1.48% net improvement and landed at 27 overall. Its strongest Agent Arena category is Code, where it ranks 15 with +4.83% net improvement. Haiku 5.5 shows cost efficiency with a median cost per task of $0.09, placing it just outside the Agent Arena Pareto frontier. Nearby on the Pareto frontier, GPT-6 Luna (Max) delivers +3.19%

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Claude Haiku 5.5 (High) by @AnthropicAI has landed in the Agent Arena! Across +20.5K real-world agentic sessions from our global community of users, this release scored +1.48% net improvement and landed at 27 overall. It's strongest Agent Arena category is Code, where it ranks 15 with +4.83% net improvement. Haiku 5.5 shows cost efficiency with a median cost per task of $0.09, placing it just outside the Agent Arena Pareto frontier. Nearby on the Pareto frontier, GPT-6 Luna (Max) delivers +3.19%

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(max) offers the strongest performance for its cost: At $0.81 per task it scores 49%, matching Claude Fable 5.1 (max with fallback). This means it achieves similar performance to leading Anthropic models, at less than a tenth of the cost per task Thank you to @silasalberti, @ybenpan, @cl571128, and the rest of the team at Cognition for building FrontierCode and collaborating with us to bring it to Artificial Analysis - we’re working to add FrontierCode to our coding agent leaderboard very soon.

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Real-world results are in for Claude Haiku 5.5 (High) by @AnthropicAI. Debuting at 30 with 1587 pts in Code Arena: WebDev, this release sits just outside the Pareto frontier while still offering strong cost efficiency. It matches GPT‑6 Luna’s price at $0.10/$0.50 per 1M input/output tokens while scoring six pts higher. Compared with other Claude models, Haiku 5.5 offers: - 90% lower cost than Haiku 4.5 at +257 pts - 95% lower cost than Sonnet 5.5 (High) at -128 pts - 98% lower cost than Opus 5 (

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ed to Teslas and Waymos, so maybe that played a role,” Ramabadran tells me. The engineers tried testing SpaceXAI’s Grok before experimenting with the latest models from OpenAI and Anthropic. At first, the models refused to take control of the vehicles—they spat back things like, “I can help interpret road images, but I can’t issue motion commands to a physical car.” But with a little careful prompting, they could be coaxed into going further. The three were stunned when models began driving. The

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Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 afte

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t are converging right now, creating fertile ground for discoveries and techniques like MetaInfer. Models are getting smarter and better at long-horizon tasks, with the release of Anthropic’s Mythos 5 and Fable 5 models marking a meaningful step forward on the kind of tasks that can be reliably delegated to AI systems. The last such moment I remember was the release of Opus 4.5 in late 2025, at which point many engineers, myself included, moved from using AI coding tools as a spicy autocomplete

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t are converging right now, creating fertile ground for discoveries and techniques like MetaInfer. Models are getting smarter and better at long-horizon tasks, with the release of Anthropic’s Mythos 5 and Fable 5 models marking a meaningful step forward on the kind of tasks that can be reliably delegated to AI systems. The last such moment I remember was the release of Opus 4.5 in late 2025, at which point many engineers, myself included, moved from using AI coding tools as a spicy autocomplete

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Claude Sonnet 5.5 by @AnthropicAI just landed at 3 in the Agent Arena. This model has a median cost per task of $2.74, and a +12.5% net improvement score. Claude Sonnet 5.5 delivers top-tier performance, but at a cost premium: 2 Claude Opus 5.5 costs $1.58 per task while achieving a higher score. That tradeoff keeps Sonnet 5.5 just off the Agent Arena Pareto frontier.

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nded in the Code Arena: WebDev. With 1786 pts, its ranked 3! At a blended $8/M tokens, Claude Sonnet 5.5 remains on the Pareto frontier with xHigh reasoning. This release is just 2 pts from GPT-6 Astra in the 2 spot with 1788 pts, for 80% of the price. By domain, Claude Sonnet 5.5 (xHigh) landed: - 2 in Gaming, Reference-Based Design, and Brand & Marketing - 3 in Simulations - 4 in Content Creation Tools and Consumer Product - 6 in Data & Analytics Congrats again to @AnthropicAI on this release!

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管理你上下文的SaaS工具，（c）或者问你为什么还没试试几秒前刚发布的新模型——我们今天不会这么做。 相反，我们将带你了解真正有效的方法：运行一个像样的后厨。多智能体工作流。 近几周来，多智能体工作流变得实用得多，原因有几个：底层模型变得更好了，而且流行的AI编程智能体让多智能体编排更容易搭建。上个季度，OpenAI在Codex工作流中推出了更深入的编排，而Anthropic则继续扩展Claude Code和MCP生态。 不过，最大的突破是速度。Codex Spark的运行速度约为每秒1,200个token，这使得引入并行和验证步骤变得切实可行，否则这些步骤的时间成本会过高。 以一个使用Codex和Figma MCP将网站复制到Figma的示例任务为例，单智能体工作流平均每次运行耗时36.5分钟，平均需要12次人工干预（且失败率100%），而多智能体工作流每次运行耗时5.2分钟，仅需2次人工干预，并且首次尝试即成功。 多智能体工作流在架构层面解决了单智能体的天花板问题。不再是一个厨师包揽一切，而是由一位主厨接单，将其拆解为范围明确、可验证的工单，再把每一张交给一名流水线厨师执行。 主厨

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ide. We expose one OpenAI-compatible API, so swapping a model is a config change. The base URL, key, and SDK stay the same. You change the model string from openai/gpt-5.6-luna to anthropic/claude-opus-5 and the script above runs against the new model. You don’t write a new integration for each provider you want to test, which is what makes it practical to rerun your examples against a new release the week it comes out. That same one-line swap is what model routing in a multi-agent setup relies

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ine Interface (AWS CLI) installed and configured; A virtual private cloud (VPC) with at least one subnet and security group; Model access enabled in the Amazon Bedrock console for Anthropic Claude Sonnet 4.6; Finch or another OCI-compatible container tool installed locally; Python 3.10+ installed; boto3 ≥ 1.36.0 or botocore ≥ 1.43.72. Older versions lack create capacity provider, and deploy.py will fail For the complete working code, see the AgentCore samples repository on GitHub. Step 1: Define

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务——还没这么简单。 自万恶之源Robocurve用GPT-6 Astra控制机器人开始，机器人/具身圈几乎一夜之间进入了“大模型时代”。 总的来看，目前主要有几条路线： 第一条是Robocurve最早采用的方案：大模型直接接收相机画面和机器人状态，输出夹爪的目标位置、朝向和开合程度，再由底层逆运动学模块转换成关节动作执行，完成后重新观察，形成闭环。 第二条路线是让大模型充当System 2（慢思考），搭配负责生成动作的VLA。Anthropic就测试过让语言模型接收、修改甚至替换MolmoAct提出的动作。 这次的HomeBody则选择让System 2直接调度技能库，再由包括预训练AMO在内的底层系统负责执行与身体协调。 它不是简单地「多接一层System 0」，而是换了一种连接决策与动作的方式，再结合空间记忆，把任务从桌面扩展到整个房间。 目前，Robocurve的最新战况是：最新的GPT-6 Sol平均得分是GPT-5.6 Sol的1.6倍，成本却低了约47%。 Opus 5.5的平均得分约为Opus 5的1.8倍，与GPT-6 Astra接近，单次成本比Astra低约21%。

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s scaling Kubernetes; the innovations described would have been simply impossible if they were renting from a neocloud who didn’t let them into the K8s control plane. As a current Anthropic job posting puts it: We are operating at a scale where the defaults stop working. We own the scheduler and extend it to place topology-sensitive ML workloads across thousands of accelerators at once. We scale the control plane itself — apiserver, etcd, controllers — so it stays responsive as object counts and

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per-token computation. These computations iteratively update the network’s internal, or latent, representation. For insight into what’s going on inside these sequence models, see Anthropic’s “A Mathematical Framework for Transformer Circuits” (2021, but still undefeated). A single forward pass through such a neural network produces both substantial internal state and a probability distribution over the next token(s) in the sequence for each sequence position. Because we predict (”regress”) base

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h their own customers: Salesforce, Zocdoc, SAP, telli. Pipecat's lean toward companies that build tooling or reference architectures for other developers: NVIDIA's Blueprint, AWS, Anthropic 3. Can I get a working agent going today? When evaluating each framework it's important that you can easily build (and reason about the code for) a voice or video agent. Given AI-augmented development, a developer should be able to get an agent working in a day with either. Independent write-ups comparing the

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ence (AI) agents we could interact with in natural language. But an AI agent restricted to only a model’s training data, without those two dynamic capabilities, is of limited use. Anthropic developed the Model Context Protocol to standardize communication between LLMs and external systems. They released it as an open standard in November 2024. Most major LLM developers quickly adopted it. Anthropic then donated the standard to the Agentic AI Foundation in 2025. MCP gives large language models a

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APIs have been the standard software communication method for decades. Developed by Anthropic in 2024, the Model Context Protocol (MCP) is an open standard that lets an AI agent discover and use tools. Deciding whether to use MCP versus an API depends on the purpose of your system, but you can often use both. What are APIs and the MCP? APIs and MCPs are connection standards that solve different problems. An API lets software programs share data through fixed rules. The MCP helps AI models use to

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areer Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play invoice tennis with startup's $17,600 Claude bill; Starlink's plan to avoid orbital near-misses: Ephemeris sharing; Shai-Hulud worm makes jump to AI infrastructure with

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