Paris, let's chat. Come join us for a tech meetup next week, where we'll be discussing our research & engineering! Hear from our director of training integration, and speak with recruiters about life at Cohere. See you there 🇫🇷 Sign up here: luma.com/giczviut

Applications are now open for two new Anthropic Fellowships. STEM Fellows (science and engineering) and Conceptual Reasoning Fellows (philosophy, cognitive science, economics, policy, and law) will spend four months at Anthropic from January 2027, working to help Claude reason well about some of the hardest problems in those fields. For more information, see the descriptions below. The application deadline is October 28. Conceptual Reasoning Fellows: job-boards.greenhouse.io/ant… STEM Fellows: job-boards.greenhouse.io/ant…

Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵

Windows now has sandboxing! Microsoft released an open-source repo, mxc, for sandboxed code execution. We collaborated with Windows to add mxc OS level sandboxing to Unsloth which adds just <100 ms of overhead. GitHub: github.com/unslothai/unsloth Guide: unsloth.ai/docs/new/studio/s…

GPT-6 and Intelligent UI, now rolling out in ChatGPT for everyone. Intelligent UI in ChatGPT delivers fast, interactive answers that make everyday questions more visual, complex topics easier to grasp, and interactive tools for your task available on the spot.

We’re beginning a process of publishing more frequent reports on model behavior, beyond what appears in our system cards and regular risk reports. Today’s report describes four types of behaviors we’ve identified during evaluations and internal use. In each, Claude acted on real websites or systems in ways we didn’t intend, sometimes by working around a restriction instead of stopping. All cases had minimal real-world impact. From an alignment and security perspective, we consider these behaviors significantly less severe than the cybersecurity incidents we reported in July and September. Read the full report: anthropic.com/research/inves…

HiDream-O1-Video-1.0 debuts at #6 on the Artificial Analysis Image to Video with Audio Leaderboard HiDream-O1-Video-1.0 is the new video model from HiDream, which describes itself as being "focused on generative AI and native omnimodal world models". HiDream positions it as a native omnimodal video model built for physical consistency. It generates 1080p videos of 5 to 20 seconds with natively synchronized audio, with the duration set by the scene rather than fixed in advance. In the Artificial Analysis Video Arena, HiDream-O1-Video-1.0 ranks #6 in Image to Video with Audio. It sits just behind Dreamina Seedance 2.0 720p, in a close group with MiniMax H3, Vidu Q4 Preview and Gemini Omni Flash, and ahead of Wan 3.0. HiDream-O1-Video-1.0 is priced at $5.80 per minute of 1080p video with audio (about $0.10 per second) on the HiHarness API. HiDream-O1-Video-1.0 is available via the HiHarness API and in vivago R1 Studio. Congratulations to @HiDream_AI on the release! See below for example outputs of HiDream-O1-Video-1.0 in the Artificial Analysis Video Arena 🧵

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.