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d our gpt-oss usage policy. Developed with feedback from the open-source community, these text-only models are compatible with our Responses API and are designed to be used within agentic workflows with strong instruction following, tool use like web search and Python code execution, and reasoning capabilities—including the ability to adjust the reasoning effort for tasks that don’t require complex reasoning. The models are customizable, provide full chain-of-thought (CoT), and support Structure

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propriate safeguards, and improving how we measure and report model performance for younger users. 3.2 Vision We ran the image input evaluations originally introduced with ChatGPT Agent. These tests use disallowed requests that combine text and images and measure whether the model’s responses are classified as safe. GPT‑6 Sol (October) and GPT‑6 Luna (October) performed on-par with GPT-5.6 Sol (August) and GPT-5.6 Luna (August), respectively, except for a regression on the extremism vision evalu

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des performance information for GPT-6 Astra on the new, updated versions; September 29, 2026: We added an Appendix: dots with information about dots, remarkably capable, always-on agents built to handle everything. Dots are built on existing model and agent capabilities, powered by GPT-6 Astra 1. Safety Overview: GPT-6 Astra Today, we are releasing GPT-6 Astra, the most capable model we have ever broadly deployed. Astra is our first model to reach the Critical level of cybersecurity capability u

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18 evaluations (higher is better) GPT‑6.1 Sol scores higher than GPT‑6 Sol in five of six categories; the regression on the gore evaluation is not statistically significant. 3.1.3 Agentic Safe Completions Table 3: Agentic Safe Completions (higher is better) We find that GPT-6.1 Sol is generally better than GPT-6 Sol at recognizing potential harms in agentic requests and avoiding taking harmful actions. 3.2 Vision Table 4: Image input evaluations, with metric safe completion (higher is better) Ac

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otein engineering) were asked to transcribe biological protocols they have personally used in the lab. Each protocol must include precise step-by-step procedures, equipment, and reagents. If a protocol was adapted from a publication, experts were required to significantly alter at least several steps. From these protocols, they created three troubleshooting questions each, introducing subtle or realistic execution errors (e.g., improper homogenization technique) and describing the resulting fail

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a Model API and Muse Code. Muse Code installs with curl dev.meta.ai/install.sh | bash (@alexandr wang). Positioning. Meta pitches the models as SOTA for “personal and professional agentic work.” @alexandr wang named three headline features: Active Memory: memory across days, weeks and months. Active Focus: working through blockers to finish tasks. Domain expertise: SOTA in finance and legal, “strong in health.” Training story. Wang says Meta continued scaling both pre-training and RL since Muse

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; Reply 0 Meta Platforms announced Oct. 13 that it has unveiled its latest frontier artificial intelligence (AI) models, the Muse Wonder series, featuring major improvements in AI agents' ability to independently handle complex tasks. The series comprises two versions, Muse Wonder and Muse Wonder Full. The models are differentiated by their availability to external developers and their use within Meta's proprietary work environment. Both are available only through paid subscriptions priced at $5

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[[ Meta新旗舰模型Muse Wonder在长程编程等Agent测试中超越Claude Opus 5.5，综合智能指数逼近第一梯队，但推理成本大涨183%，且3D生成实测表现不佳，暴露视觉空间推理短板。 要点 1. 旗舰跑分挤进第一梯队，长程编程领先 Wonder Full综合智能指数56分，距Claude Opus 5.5仅差2分；DeepSWE 1.1长程软件工程测试中取得79.0分，超过Opus 5.5的74.2分和GPT-6 Astra的74.1分，在10项测试中有6项跻身前三。 2. 换用新预训练底座，针对Agent能力优化 Wonder内部代号Watermelon，采用独立更大规模预训练并引入Active Memory与Active Focus机制，从底层增强模型的长期记忆、持续执行与任务泛化能力，而非像Spark系列那样仅依靠后续强化学习迭代。 3. 推理成本大幅上涨，颠覆性定价策略引发质疑 Wonder任务平均成本达4.52美元，较上代Spark 1.3上涨183%，比GPT-6 Astra高39%；官方虽小幅降低按Token计费价格，但Full版本的订阅额度消耗异

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r Full新模型至少在部分基准测试上，已能与GPT-6 Astra和Claude Fable 5.1掰手腕。 第三方机构Artificial Analysis的测试也显示，Wonder Full较上一代脱胎换骨，在多项智能体和专业工作评测中跻身前三甲。 花大价钱攒起来的队伍，让Meta重新有了跟领先模型叫板的底气，也让Meta Muse的Personal Agent之路能走得更顺畅些。 Meta此次发布，延续了一年多来对AI业务的重整。 2025年4月，Llama 4马失前蹄，扎克伯格随即加大人才和算力投入，招揽Scale AI创始人Alexandr Wang，重组研究团队。今年4月，重组后的团队推出Muse Spark，但与竞争对手相比仍差着一口气。 按照Meta的说法，过去14个月里，团队推倒重来，从底层重建技术体系，并持续扩大预训练和强化学习的规模。此次Wonder系列在吉瓦级Prometheus数据中心完成训练，成为这轮豪赌交出的最新筹码。 Muse Wonder围绕两个方向动刀，分别是Active Memory和Active Focus。 Active Memory负责跨对

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The Muse Wonder series achieves state-of-the-art results across key benchmarks for personal and professional agents, memory, and coding. Muse Wonder and Muse Wonder Full apply specialized knowledge to problems people encounter at work and in daily life, from understanding a lease to making sense of financial or health information. Our evaluations include tasks in law, finance, and health that test this ability.

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We’re excited to introduce the Muse Wonder series with Muse Wonder and Muse Wonder Full, our most capable models yet. Both are frontier models, built to deliver state-of-the-art performance for personal and professional agentic tasks.

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same update cut Clef-flash pricing below TypeSafe’s Jev and made hosted Clef roughly 2x faster. For developers, this removes the transcription and captioning steps that multimodal agent pipelines usually need. TL;DR Size: 30B total, 3B active parameters (MoE); 64,000-token context on Workers AI; Runs on: Workers AI as @cf/cloudflare/clef-omni; self-hosting tested on a single H200, about 64 GB GPU memory in bfloat16; Performance: Close to Clef on routing and intent tasks, behind it on most reason

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d variants, hardware not disclosed; Performance: Vendor-reported scores are near ceiling on cyber benchmarks and strong on coding; Best: 100% on Cybench (39/39 tasks, unrestricted agent execution); Worst: 76.5% on SWE-bench Pro V2, its lowest published score; Bottom line (best): Top-tier cyber scores at $3.00 / $7.50 per 1M tokens; Bottom line (worst): Every number is self-reported, and CVE-Bench used only 24 evaluable tasks What is OrcaCyber Zero 1.5? OrcaCyber Zero 1.5 is a frontier cybersecur

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owcase what Inkling can do and how it measures up on important qualities such as trustworthiness and safety. Generalist model Inkling is designed to be broad. We trained it across agentic, reasoning, coding, instruction-following, factuality, vision, and audio tasks, rather than narrowly optimizing for one domain. That breadth matters for customization and real-world use: different users need models that can adapt to very different workflows, not just excel on benchmarks. Agentic coding and tool

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and well-rounded performance across a range of benchmarks. Inkling-Small Inkling Compared to Inkling, Inkling-Small achieves comparable performance with much less compute. Across agentic tool use (Terminal-Bench 2.1), reasoning (HLE text-only, no tools), and instruction following (IFBench) benchmarks, Inkling-Small is more efficient than Inkling and competitive with other models in its weight class. Furthermore, its variable thinking effort lets users easily adapt it to target their use case, b

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ducing Beam, Reflection’s first open-weight model. Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads. Beam’s capabilities come from major investments in both pretraining and reinforcement learning (RL). We pretrained the model on 23.8 trillion diverse, curated, high-quality tokens from the web and proprietary licensed datasets, matching or outperforming available similar-sized open base models. In pa

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on-1 is built for fast, structured decisions. According to Microsoft , it handles classifications, evaluations, and routing decisions and has "the potential" to "guide and control agents through complex environments." Decision-1 is based on Qwen3.5-9B. Microsoft says it's the most accurate model tested across 36 benchmarks covering nearly 150,000 questions. On speed, it's 2.5 times faster than the runner-up, H2O-Lightning-4B. Cloudflare's open-source Clef models, which are also based on Qwen, we

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c.twitter.com/M3fAOD90r6 — Satya Nadella (@satyanadella) October 9, 2026 Xbox Research used it to sort more than 10,000 pieces of player feedback, the Copilot team graded chat and agent responses with it, on-call engineers used it to pull context during live incidents, and Microsoft Discovery used it to score experiments before an agent replans. Microsoft’s numbers show it’s more than 14 times as fast as GPT-6 Sol at a fraction of the cost for Xbox, and 46 times more consistent in Discovery. But

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unlocks advanced photo-editing tools, the all-new Image Playground, and more; AFM 3 Cloud Pro, our most capable server-based model, which powers our most demanding use cases, like agentic tool use and complex reasoning AFM 3 Core, AFM 3 Core Advanced, and AFM 3 Cloud, along with ADM 3 Cloud, are all purpose-built for Apple silicon. For AFM 3 Cloud Pro, we worked with Google and NVIDIA to extend Private Cloud Compute to NVIDIA GPUs in Google Cloud, while maintaining the same guarantees to protect

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Nace.AI has open-sourced Drex 1.5, a 9B decision model for agents and backend workflows. The Drex 1.5 decision model does not write text. It reads a state and typed questions, then returns a probability for every option. Nace reports 58.08 on the public Decision Index 0.3.1, the top score under 10B parameters. Weights are on Hugging Face, and a hosted version is live on OpenRouter. TL;DR Size: 8.95B parameters (dense), bf16 weights about 18 GB. Context is 16,384 tokens by default, up to 131,072;

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