全部 AI 动态

Claude Dashboards and Claude Motion are in beta today. Ask Claude to turn your data into live dashboards and your ideas into animated explainers.

Introducing Voyager: the open harness for S-tier video, graphics and games. The Codex for creative work. Most AI tools are built for coding. Voyager is designed to get the best creative results from models like Opus, Astra and DeepSeek. It comes with free graphics, music and video editing tools - or, it can drive Blender, Resolve, After Effects, Ableton and 100+ others. Everything in this thread was made with Voyager. A few of our favorites:

Cursor can now build charts and diagrams right in the chat. Use /visualize to analyze data and see the answer inline. Available now in the Agents Window.

Video 1.5 Lite is live in the Grok Imagine API. The new workhorse for text-to-video and image-to-video. $0.02/sec at 480p $0.03/sec at 720p $0.14/sec at 1080p

隆重推出 Claude Haiku 5.5：我们迄今为止发布的最便宜、最快、最强大的小型模型。 平均而言，它的运行成本比 Claude Haiku 4.5 低约 75%。

Today we are announcing Harvey LAB-AA v1.1 in collaboration with Harvey. This updates our scoring methodology for the Legal Agent Benchmark (LAB) to add a hallucination check and require correct responses to not include material misstatements. LAB-AA v1.1's new headline metric, Hallucination-Gated All-Pass Rate, only credits a task when the deliverables satisfy every rubric criterion and contain no material hallucinations. We define a material hallucination as one that would mislead a reader on a substantive point, such as a wrong contractually required date, while a minor hallucination is a real error that is unlikely to meaningfully affect the legal interpretation of a deliverable. Minor hallucinations are reported separately and do not affect the headline score. Grok 4.7 (xhigh) leads at 9.4% Hallucination-Gated All-Pass Rate, and >60% of otherwise passing results across the models tested at launch contain a material hallucination. This is the first step in enhancing the methodology for Harvey LAB-AA. In future updates, we’re working with Harvey to better account for the full set of factors lawyers value, including usability features like style and tone. Key takeaways: ➤ Top of the leaderboard: Grok 4.7 (xhigh) from @SpaceXAI leads at 9.4% Hallucination-Gated All-Pass Rate, narrowly ahead of Muse Spark 1.3 (max) from @AIatMeta at 8.9% and GPT-6 Astra (max) from @OpenAI at 8.6% ➤ Hallucinations reshape the leaderboard: without the hallucination gate, Muse Spark 1.3 (max) would lead clearly with a 26.7% all-pass rate, but two thirds of those passes contain one or more material hallucinations, resulting in a Hallucination-Gated All-Pass Rate of 8.9%. GPT-6 Astra (max) retains almost all of its passes after the hallucination gate (8.9% to 8.6%) and moves from joint 10th to 3rd ➤ The GPT-6 model family is the most grounded: GPT-6 Astra (max) averages 0.03 material hallucinations per task (4 across all 120 tasks) and GPT-6 Sol (max) averages 0.07. When comparing six checker models on a 20-task subset, GPT-6 Astra had zero material hallucinations under every checker, including Claude Opus 5.5 (high). By comparison, Gemini 3.8 Flash (high) averages 13.96 material hallucinations per task. ➤ Top-scoring models aren’t the most expensive: Grok 4.7 (xhigh) takes the top spot on the leaderboard at a cost of ~$9.50 per task, under half the cost of Claude Fable 5.1 (max with fallback) at ~$21.70, the most expensive model. Muse Spark 1.3 (max) represents strong performance for its cost, landing in second for ~$4.20 per task Thank you to @nikogrupen and @ItsJulioPereyra from @harvey for their work on Harvey LAB and collaboration.

This feels unreal Used Grok Bot today, mostly from my phone I got more done from my phone with Grok Bot than I usually do sitting at my desk - Colab notebooks for Google EmbeddingGemma 2, D1 by LiquidAI inference - Colab notebooks for open source decision models finetunes using Qwen3.5-4B and Gemma 4 E4B as base - Put up a Grok Bots page on my website with all the templates I've been sharing - Worked on the MiniMax H3 face-swap clip - Figured out what's eating my Windows drive and how to free space - Created Security bot and told it to treat outside content as hostile, especially anything that might touch my computers - Worked with my stocks bot on scheduled market updates, news and recommndations

Ultrafast is rolling out today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work. Near-Astra intelligence at up to 8x faster speeds than Sol Standard, so you can build as fast as the ideas come.

Effective November 12th, 2026, abusive behavior towards Claude will be a violation of Anthropic's Usage Policy.

Stop pinging me and let my Codex talk to yours. That's why we built @alook_ai — think of it as Grok Bot meets Discord in one open-source product. Take alook at https://alook.ai, or host it yourself. 1/6

Ultrafast is rolling out today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work. Near-Astra intelligence at up to 8x faster speeds than Sol Standard, so you can build as fast as the ideas come.

Ultrafast is rolling out today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work. Near-Astra intelligence at up to 8x faster speeds than Sol Standard, so you can build as fast as the ideas come.

Today we are announcing Harvey LAB-AA v1.1 in collaboration with Harvey. This updates our scoring methodology for the Legal Agent Benchmark (LAB) to add a hallucination check and require correct responses to not include material misstatements. LAB-AA v1.1's new headline metric, Hallucination-Gated All-Pass Rate, only credits a task when the deliverables satisfy every rubric criterion and contain no material hallucinations. We define a material hallucination as one that would mislead a reader on a substantive point, such as a wrong contractually required date, while a minor hallucination is a real error that is unlikely to meaningfully affect the legal interpretation of a deliverable. Minor hallucinations are reported separately and do not affect the headline score. Grok 4.7 (xhigh) leads at 9.4% Hallucination-Gated All-Pass Rate, and >60% of otherwise passing results across the models tested at launch contain a material hallucination. This is the first step in enhancing the methodology for Harvey LAB-AA. In future updates, we’re working with Harvey to better account for the full set of factors lawyers value, including usability features like style and tone. Key takeaways: ➤ Top of the leaderboard: Grok 4.7 (xhigh) from @SpaceXAI leads at 9.4% Hallucination-Gated All-Pass Rate, narrowly ahead of Muse Spark 1.3 (max) from @AIatMeta at 8.9% and GPT-6 Astra (max) from @OpenAI at 8.6% ➤ Hallucinations reshape the leaderboard: without the hallucination gate, Muse Spark 1.3 (max) would lead clearly with a 26.7% all-pass rate, but two thirds of those passes contain one or more material hallucinations, resulting in a Hallucination-Gated All-Pass Rate of 8.9%. GPT-6 Astra (max) retains almost all of its passes after the hallucination gate (8.9% to 8.6%) and moves from joint 10th to 3rd ➤ The GPT-6 model family is the most grounded: GPT-6 Astra (max) averages 0.03 material hallucinations per task (4 across all 120 tasks) and GPT-6 Sol (max) averages 0.07. When comparing six checker models on a 20-task subset, GPT-6 Astra had zero material hallucinations under every checker, including Claude Opus 5.5 (high). By comparison, Gemini 3.8 Flash (high) averages 13.96 material hallucinations per task. ➤ Top-scoring models aren’t the most expensive: Grok 4.7 (xhigh) takes the top spot on the leaderboard at a cost of ~$9.50 per task, under half the cost of Claude Fable 5.1 (max with fallback) at ~$21.70, the most expensive model. Muse Spark 1.3 (max) represents strong performance for its cost, landing in second for ~$4.20 per task Thank you to @nikogrupen and @ItsJulioPereyra from @harvey for their work on Harvey LAB and collaboration.

我们很兴奋地加倍投资 @arena 的 2 亿美元 B 轮融资。 随着 AI 模型数量激增，了解它们在现实世界中的实际表现正变得越来越重要。 Arena 还推出了其 Alignment Index，用于衡量 AI 行为在现实环境中与人类价值观的一致程度，这有助于实现 Arena 的使命——通过使用 AI 的人的体验来衡量 AI。 我们最初在种子轮就与这个团队合作。自他们的 A 轮以来，Arena 的年化收入已超过 1 亿美元，数百万人通过现实世界中的使用帮助评估前沿模型。 这种增长印证了我们最初的投资论点：模型越多，关于其表现的独立信号就越有价值。 祝贺联合创始人 @ml_angelopoulos、@infwinston 和 @istoica05。随着模型竞赛愈发拥挤，Arena 的工作正变得越来越有趣。 CC: @buckymoore, @jamesalcorn94, @ravi_lsvp, @prathikhosalli

Thrilled to welcome @SpaceXAI as a Founding Corporate Patron for the Omacom Foundation. That's $1,500,000 in @grok tokens for the maintenance and development of Omarchy. Maybe soon they'll be beamed straight from orbit? Let's go 🚀🌕 https://omarchy.org/news/2026/10/spacexai-joins-as-founding-corporate-patron/

Ultrafast is rolling out today for GPT-6.1 Sol in the API, Codex, and ChatGPT Work. Near-Astra intelligence at up to 8x faster speeds than Sol Standard, so you can build as fast as the ideas come.

Grok Bot can now help run your Shopify store. Connect Shopify, then ask it to check orders, track inventory, and keep product listings up to date.

Today we're launching Odyssey-3, the most powerful foundation world model yet. It sets a new state of the art on Physics-IQ, and powers robots, trains AIs, and generates interactive experiences. It's really cool. Experience the model today, all for free!

Thrilled to welcome @SpaceXAI as a Founding Corporate Patron for the Omacom Foundation. That's $1,500,000 in @grok tokens for the maintenance and development of Omarchy. Maybe soon they'll be beamed straight from orbit? Let's go 🚀🌕 https://omarchy.org/news/2026/10/spacexai-joins-as-founding-corporate-patron/

Computer created explainer videos for all 722 preprints in OpenAI's mathematical manuscript release, totaling 93 hours of video. Watch the videos on YouTube: https://722.video Here's how Computer tackled this task:

当 @Felicis 最初在种子轮投资 @Arena 时，我们相信这家公司正在构建某种不可或缺的东西：一个独立、由社区驱动的真相来源，用于衡量 AI 前沿。这份信念只增不减，我们随后领投了 A 轮。 自那以来的进展非同凡响。Arena 的年化收入已突破 1 亿美元，促成了 3.5 亿次会话和 6200 万次投票，并成长为全球最大的 AI 社区之一。 今天，Arena 宣布完成 2 亿美元的 B 轮融资，估值达 31 亿美元，同时推出了一项新的对齐指数（Alignment Index），用于衡量 AI 智能体的行为是否安全、真实，并且不超出用户实际要求它们做的事情的范围。 随着 AI 系统开始代表我们采取实际行动，仅仅衡量能力已经不够了。我们还需要知道这些系统是否值得信任。Arena 拥有独特的优势，能够基于数百万真实世界的人类交互，为行业给出这个答案。 我们坚定地相信 @ml_angelopoulos、@infwinston 以及整个 Arena 团队，他们正在为 AI 构建可信的衡量层。 https://arena.ai/blog/series-b

@perplexity_ai’s Decider V1.1 ranked #1 on http://decisionbench.ai with lowest cost! 949 shared text cases: 93.9% accuracy · 534 ms median · $0.016 / 1k decisions Thanks @AravSrinivas & perplexity team for building - looks really good, we're going to try it for our product use cases Our latest results live on https://decisionbench.ai https://x.com/rohanatlan/status/2103188107143307541