Google has made Gemini 4 Argon generally available on the Gemini API, AI Studio and the Gemini app. The sparse MoE model writes up to 1M output tokens and leads the Vals Index at 68.90%. The post Google Releases Gemini 4 Argon API With 1M Token Output Limit appeared first on MarkTechPost .
Meta released Muse Wonder and Muse Wonder Full, 1M-context agent models. Muse Wonder costs $4/$20 per million tokens on Meta Model API. Wonder Full, on Muse Code plans, leads DeepSWE and SWE-Marathon. The post Meta Launches Muse Wonder and Muse Wonder Full: Benchmarks, Pricing and How to Use It appeared first on MarkTechPost .
Release: llm-meta-ai 0.2 Meta AI release Muse Wonder (and Muse Wonder Full), their new "most capable models". These look like they may be Meta's first Fable class models - Artificial Analysis scores Muse Wonder (Max) as equivalent to GPT-6.1 Sol. I updated my llm-meta-ai plugin for the new model. Here are pelicans for Muse Wonder for the six reasoning levels: minimal, low, medium, high, xhigh, max. This is the max one: Blue cycling helmet, bicycle is missing one bar but otherwise an OK shape, has mudguards, nice background, OK beak. For comparison, here are pelicans for Muse 1.3 (released September 2nd) and Muse 1.2 (released August 5th). Meta AI are putting out a new model a month, and the improvements between each model (at least in terms of drawing pelicans) are material. Tags: ai , generative-ai , llms , meta , pelican-riding-a-bicycle , llm-release
发布：llm-gemini 0.35 今天通过 Gemini API 以及其每月 100 美元订阅计划向客户提供了全新的 Gemini 4 Argon 模型，我为 llm-gemini 添加了对它的支持。下面是低、中、高三档生成的 pelican。如预期一样，它们都非常出色：Gemini 一直以来都很擅长生成优秀的 SVG，而且据我所知，这是唯一一家其员工专门投入 SVG 研究的实验室（他们以前也曾展示过自己生成的 pelican）。这是“高”档生成的插图，花费了我 17.166 美分： 时尚而准确的自行车车架。篮子里有一条鱼。帅气的围巾。双脚分列车架两侧，踩在脚踏板上。车轮辐条、一顶小帽子、漂亮的喙和喉囊，以及略显凶狠的表情。 定价很有意思。发布时，Argon 的价格是输入每百万 token 2 美元、输出每百万 token 10 美元——与 GPT-6 Sol 相同，是 Claude Opus 5.5 价格的一半，以及 Claude Fable 5.1 和 GPT-6 Astra 价格的五分之一。但从 12 月 1 日起，Argon 的价格将翻倍——届时会与 Opus 5.5 同价。 如今，这样的价格并不能真正直接比较，因为真正重要的是：在代理式运行框架中，模型要完成一个目标需要消耗多少 token。Artificial Analysis 发布了使用各模型运行其基准测试的总成本，这为我们提供了一个有用的参考。Argon 在这方面表现非常好：运行该基准测试的成本为 2,407 美元，接近 GLM-5.3，而且明显低于 OpenAI、Anthropic 和 Meta 的最新模型。即使价格翻倍，它仍会比 GPT-6 Astra 略便宜。 Artificial Analysis 的堆叠柱状图截图，对 14 个 AI 模型的美元成本进行比较，按从低到高排序；柱体按提供商着色，每个柱体下方显示提供商徽标，底部以倾斜文字标出模型名称。右上角的徽标为 Artificial Analysis。指向 Gemini 4 Argon 柱体的悬浮提示框显示：Gemini 4 Argon（High），2,407 美元；输出 313 美元；推理 815 美元；缓存写入 897 美元；缓存读取 264 美元；非缓存输入 118 美元；点击查看更多信息。 从左到右的柱体如下，每个柱体上方标有总成本，部分分段数值印在柱体内部：Qwen3.8 27B（xhigh），1,336 美元，Alibaba 徽标；Mistral Large 4 Preview，1,607 美元，Mistral 徽标；Gemini 3.8 Flash（high），1,623 美元，Google 徽标；Muse Spark 1.3（max），2,000 美元，其中一段为 769 美元，Meta 徽标；Gemini 4 Argon（high），2,407 美元，其中两段分别为 815 美元和 897 美元，Google 徽标；GLM-5.3（max），2,503 美元，其中一段为 790 美元，Z.ai 徽标；Kimi K3（max），3,658 美元，Kimi 徽标；Qwen3.8 Max（0902），4,935 美元，其中两段分别为 893 美元和 1,441 美元，Alibaba 徽标；Grok 4.7（xhigh），4,967 美元，其中两段分别为 1,861 美元和 1,618 美元，xAI 徽标；GPT-6 Astra（max），5,324 美元，其中两段分别为 778 美元和 978 美元，OpenAI 徽标；Muse Wonder（max），5,733 美元，其中一段为 1,748 美元，Meta 徽标；Claude Sonnet 5.5（max with fallback），7,260 美元，其中两段分别为 1,216 美元和 1,815 美元，Anthropic 徽标；Claude Opus 5.5（max with fallback），8,708 美元，其中一段为 921 美元，Anthropic 徽标；Claude Fable 5.1（max with fallback），13,129 美元，其中一段为 7,219 美元，Anthropic 徽标。 （我用 ChatGPT 编辑了那张图表！数据全部正确，我已经核对过了——我只是需要让 Gemini 4 Argon 的信息框显示出来，同时避免遮挡其他数字。） 标签：google，ai，generative-ai，llms，llm，gemini，llm-pricing，pelican-riding-a-bicycle，llm-release
Google LLC today brought its Gemini 4 Argon frontier artificial intelligence model to Gemini app users on its Pro and Ultra subscription plans, two weeks after the model went first to vetted cybersecurity defenders. Subscribers on those plans also get a new tasks mode that keeps Gemini working on a job after they have stepped […] The post Gemini 4 Argon reaches Gemini app Pro and Ultra subscribers alongside a new tasks mode appeared first on SiliconANGLE .
Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI
Existing benchmarks measure solve rate and token output/cost, but don't punish excess code, which makes a codebase harder to work with in the long term. I wanted to measure this since it matters most for long-term projects. Method: it takes the patches that benchmarks already publish (DeepSWE, SWE-bench Verified, SWE-bench Live) and counts "coding units": syntax elements like statements, calls, names and literals. I didn't use lines of code, so cramming code isn't rewarded. Deleted code is subtracted, so it measures net units. GPT-6 and Claude 5.5 have no public patches yet, so I ran them myself on 3–10 tasks on my own subscriptions (shown as faded circles, not ranked). Any feedback or model contributions are welcome, especially public patch exports for newer models. GitHub: https://github.com/La5u/Parsimony Not affiliated with any of the benchmarks. Comments URL: https://news.ycombinator.com/item?id=50077619 Points: 2 # Comments: 0
Google is making Gemini 4 Argon available worldwide through its API, the Gemini app, and Google Antigravity. The frontier model is aggressively priced but trails Anthropic's Claude Opus 5.5 and Meta's Muse Wonder Full in independent benchmarks. Internally, Google says Argon already cut coding tasks from months to weeks. The article Gemini 4 Argon is now available worldwide as Google's most capable frontier model appeared first on The Decoder .
Developers will get 50 percent discounted API rates through November.
After months sitting on the sidelines of the frontier AI race, Google has finally launched its long-awaited Gemini 4 Argon model, claiming it can compete with the best the industry has to offer. Google says Argon delivers "frontier reasoning and improved performance across complex workflows," while cutting down on hedging, repetition, and the cliches that […]
Grok 4.7 现已在 Amazon Bedrock 上可用。在本文中，我们基于 Grok 4.7 构建一个工具调用智能体，并在四个推理力度级别下对其在三项推理任务上的表现进行评估，同时衡量每个已完成任务的正确性和令牌成本。我们将展示如何针对每个请求调整这种权衡，并在生产环境中对其进行监控。
我们让开发者、企业、科学家和消费者都能使用 Gemini 4 Argon，这是我们最新的前沿模型。
AWS 工程师分享了在 Amazon SageMaker AI 上针对融合式专家混合（MoE）模型的内核优化方案：通过 Swap-AB 提升 WGMMA 利用率，利用 Triton 后端路由实现 FP8 量化，并消除 Triton JIT 重新编译。综合应用这些优化后，在 H200 GPU 上吞吐量最高提升 10%，首个令牌时间最高缩短 9%。
MSL’s emergence as a frontier lab is finally complete.
Z.ai’s GLM-Legal is a legal AI model built on GLM-5.3-Flash. It scores 19.2% all-pass on Harvey’s Legal Agent Benchmark, nearly 3x its base, at $0.15 per test. Access is by application. The post Z.ai GLM-Legal Beats Claude Fable 5 and Kimi K3 on Harvey LAB appeared first on MarkTechPost .
Meta Platforms Inc. today released Muse Wonder and Muse Wonder Full, two artificial intelligence models it says are its most capable yet at running agents that handle personal and professional tasks. Meta aimed the release at the longer, multistep jobs people hand off to an AI agent. One example it gave was working through an […] The post Meta releases Muse Wonder AI models built to run personal and professional agents appeared first on SiliconANGLE .
Alibaba’s Qwen team has introduced QwenGyre, an elastic reinforcement learning RL framework for extreme-long-horizon agent training. QwenGyre is an online reinforcement learning system from Alibaba that trains coding agents through unmodified, black-box harnesses. It targets rollouts that run for hours and process close to 1M tokens. The QwenGyre elastic RL framework was used on Qwen’s […] The post Alibaba Qwen Introduces QwenGyre: Elastic Reinforcement Learning RL for XLong-Horizon Agents appeared first on MarkTechPost .