推荐理由:官方给出 Qwen3.7-Max 在编码智能体方向的能力定位和一个月五折的上线优惠,读者可据此判断是否值得试用。
模型发布
全部主题新模型的发布、开源与迭代:大模型厂商的旗舰更新、开源权重放出、性能与价格变化的第一时间记录。
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第 441–460 条 · 共 530 条@alibaba_cloud@alibaba_cloud精选AI 评分6969 
@berryxia@berryxia精选AI 评分6868 引用dadabots (@dadabots)@dadabots🥳 Announcing Stable Audio 3 🍕 🏆 fastest music models ever 💻 runs on MacBookPro M-series 🧪 break it plz 🧠 LoRA finetune in < 1h 📷 Sm = faster, Medium = qualityer ⚡ 59x realtime on M5 Pro One-liner fast install: curl -LsSf dadabots.com/_/sa3-mac | bash Video
推荐理由:Stable Audio 3 给出在 Mac 本地运行音乐生成的安装方式与性能数据,可据此判断本地音乐生成工作流的可行性。
@alibaba_cloud@alibaba_cloud精选AI 评分7373 引用OpenRouter (@OpenRouter)@OpenRouterThe new Qwen3.7-Max from @Alibaba_Qwen is live on OpenRouter. The flagship of the Qwen3.7 series, built for agent-centric work: coding, office and productivity tasks, and long-horizon autonomous execution. Big jumps in coding and agent benchmarks over Qwen3.6, with explicit prompt caching for repeated context.
推荐理由:Qwen3.7-Max 已在 OpenRouter 上线,面向编码与办公的智能体场景,读者可了解这一旗舰版本的定位。
@OpenRouter@openrouter精选AI 评分6969 引用Qwen (@Alibaba_Qwen)@Alibaba_Qwen📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: 🧑💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it. 🗂️ A reliable office and productivity assistant. Get your work done through MCP integrations and multi-agent orchestration. ⏱️ Long-horizon autonomy. 35 hours straight on a kernel optimization task — 1,000+ tool calls, zero hand-holding. 🔌 Scaffold-agnostic. Claude Code, OpenClaw, Qwen Code, or your own stack. Consistent reliability everywhere. API's up on Alibaba Model Studio. You can also take it for a spin on Qwen Studio. Go build something wild!🏃🏃♂️ 📖 Blog: qwen.ai/blog?id=qwen3.7 ✅ Qwen Studio: chat.qwen.ai/?models=qwen3.7… ⚡️ API:modelstudio.console.alibabac…
推荐理由:原文列出端到端编码、MCP 集成与长时间自主运行等能力,可用以判断该旗舰模型在 Agent 场景中的定位。
@OpenRouter@openrouter精选AI 评分7373 
推荐理由:Qwen3.7-Max 作为千问旗舰上线 OpenRouter,面向智能体任务,并给出相对 Qwen3.6 的基准变化。
@kimmonismus@kimmonismus精选AI 评分7272 
引用Qwen (@Alibaba_Qwen)@Alibaba_Qwen📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: 🧑💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it. 🗂️ A reliable office and productivity assistant. Get your work done through MCP integrations and multi-agent orchestration. ⏱️ Long-horizon autonomy. 35 hours straight on a kernel optimization task — 1,000+ tool calls, zero hand-holding. 🔌 Scaffold-agnostic. Claude Code, OpenClaw, Qwen Code, or your own stack. Consistent reliability everywhere. API's up on Alibaba Model Studio. You can also take it for a spin on Qwen Studio. Go build something wild!🏃🏃♂️ 📖 Blog: qwen.ai/blog?id=qwen3.7 ✅ Qwen Studio: chat.qwen.ai/?models=qwen3.7… ⚡️ API:modelstudio.console.alibabac…
推荐理由:作者把 35 小时自主优化的传播印象与实际范围区分开,并单独讨论智能体能力泛化这一论断。
@alibaba_cloud@alibaba_cloud精选AI 评分6666 
推荐理由:官方披露 Qwen3.7 连续自主执行约 35 小时优化注意力 kernel 的过程,可作为观察自主编码智能体能力的参照。
@alibaba_cloud@alibaba_cloud精选AI 评分7070 
推荐理由:官方列出编码、长任务与多种脚手架兼容能力,读者可据此判断这款旗舰模型在智能体工作流中的定位。
@Google@google精选AI 评分7373 
推荐理由:官方给出 4 倍速度与不到一半成本的对比,读者可据此判断这款智能体与编码模型的定位。
@GeminiApp@geminiapp精选AI 评分7676 推荐理由:Gemini 3.5 Flash 免费向全球用户开放,给出了网页端和 App 中直接切换使用的入口。
@GoogleDeepMind@googledeepmind精选AI 评分6767 Google DeepMind 宣布 Gemini 3.5 Flash 发布,该推文由 @GeminiApp 转发,并随附一段视频。原文未给出模型能力、参数或可用范围的更多细节。

@berryxia@berryxia精选AI 评分7272 
推荐理由:作者用递归树动画实测 Gemini 3.5 Flash 的生成速度,并列出其在 Agent 榜单与多模态基准上的成绩。
@berryxia@berryxia精选AI 评分6565 
推荐理由:作者用同一递归树 Prompt 记录生成耗时与效果,并列出 Agent 与多模态榜单成绩,可快速判断该模型的能力定位。
@berryxia@berryxia精选AI 评分6666 Gemini 3.5 Flash 已在 ZenMux 上线并提供免费额度体验,也可通过 API 调用。作者用它跑递归二叉树生长测试,从输入提示词到生成完整 HTML 动画网页耗时 77.56 秒。

推荐理由:材料给出同一提示词下的生成耗时与多项榜单成绩,可作为了解 Gemini 3.5 Flash 速度与 Agent 能力的参考。
@berryxia@berryxia精选AI 评分7979
引用Artificial Analysis (@ArtificialAnlys)@ArtificialAnlysGoogle’s new Gemini 3.5 Flash is the clear leader on the Intelligence vs Speed Pareto frontier and makes large gains on GDPval-AA (real-world agentic tasks), but is 5x the cost of Gemini 3 Flash @GoogleDeepMind gave us pre-release access to Gemini 3.5 Flash, the latest model in its Flash family, which has traditionally has offered faster, lower-cost alternatives to Gemini Pro models. Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash, driven primarily by agentic performance gains and hallucination reduction. It achieves speeds of over 280 output tokens/s, but higher token usage and token pricing make it over 5x more costly to run the Intelligence Index than Gemini 3 Flash, and 75% more costly than Gemini 3.1 Pro. Gemini 3.5 Flash is $1.50/1M input and $9/1M output tokens, Gemini 3 Flash was $0.5/$3 per 1M input/output tokens, a 3x increase. The rest of the increase was driven by higher token usage when running our benchmarks Key results for Gemini 3.5 Flash with ‘high’ thinking level: ➤ 9 point Intelligence Index improvement: Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash. This places it ahead of Grok 4.3 (high, 53) and Claude Sonnet 4.6 (max, 52). The model improves across nearly all evaluations, with the largest gains coming from agentic evaluations and AA-Omniscience (knowledge and hallucination). On AA-Omniscience, Gemini 3.5 Flash improves by 11 points, driven primarily by reduced hallucinations, with its hallucination rate falling to 61%, a 31 point decrease compared to Gemini 3 Flash ➤ Agentic capability improvements: Gemini 3.5 Flash improves substantially over Gemini 3 Flash across our agentic evaluations, in both GDPval-AA (real-world agentic tasks) and Tau2-Bench Telecom (agentic tool use). Its GDPval-AA result is especially notable, achieving an Elo of 1656, well ahead of Gemini 3 Flash (1204) and Gemini 3.1 Pro (1314), and just behind GPT-5.4 (xhigh, 1674). This represents a meaningful step forward for Google in agentic performance, which has historically been a relative weakness for Gemini models ➤ Speed-intelligence frontier: Gemini 3.5 Flash achieves speeds of over 280 output tokens per second, ~70% faster than Gemini 3 Flash and models such as gpt-oss-120b and GPT-5.4 mini (xhigh). With its 55 Intelligence Index score, this places Gemini 3.5 Flash on the speed-intelligence Pareto frontier alongside Gemini 3.1 Pro and Gemini 3.1 Flash-Lite, reinforcing Google’s strength in models balancing speed and intelligence ➤ 5.5x increase in cost to run: Gemini 3.5 Flash costs $1,552 to run the Artificial Analysis Intelligence Index, 5.5x more than Gemini 3 Flash and 75% more than Gemini 3.1 Pro. This is driven by increases in both token usage and token prices. Output token usage is broadly unchanged from Gemini 3 Flash (73M vs. 72M), but input token usage increases significantly, driven primarily by an increase in the number of turns in agentic evaluations. Gemini 3.5 Flash is priced 3x higher than Gemini 3 Flash at $1.50/$9.00 per 1M input/output tokens, with a 90% discount for cached input tokens ➤ Google continues to lead multimodal performance: Gemini 3.5 Flash is multimodal, supporting image, video, and speech input alongside text. This differs from many proprietary models, including Claude Opus 4.7, Grok 4.3, and GPT-5.5, which support image input only. In our multimodal evaluation, MMMU-Pro, Gemini 3.5 Flash scores 84% - the highest score recorded. This puts models from Google in the top two spots, with Gemini 3.1 Pro scoring 82% Key model details: ➤ Context window: Retains the same 1M context window as Gemini 3 Flash ➤ Multimodality: Text, image, video and speech input with text output only ➤ Pricing: $1.50/$9.00 per million input/output tokens, with a 90% discount for cached input tokens Congratulations @GoogleDeepMind , @sundarpichai and @demishassabis on the great release!
推荐理由:借 Artificial Analysis 的预发布基准,可以看到 Gemini 3.5 Flash 在智能与速度上的提升及其成本代价。
@GeminiApp@geminiapp精选AI 评分8080 Gemini 3.5 Flash 即日起面向全球所有用户免费开放。用户可在 gemini.google 或 App 的模型下拉菜单中选择 3.5 Flash 试用。
推荐理由:Gemini 3.5 Flash 面向全球免费开放,读者可了解该模型的推送范围与使用入口。
@berryxia@berryxia精选AI 评分7676 
推荐理由:归纳了 Gemini 3.5 系列、Omni 世界模型与硬件落地的要点,可据此了解 Google 在智能体方向的推进节奏。
@berryxia@berryxia精选AI 评分7575
引用Google DeepMind (@GoogleDeepMind)@GoogleDeepMindWe’re dropping Gemini Omni: our first step towards a model that can create anything from anything - starting with video. It combines Gemini’s intelligence with our generative media systems - representing a leap forward in world understanding, multimodality, and editing 🧵 Video
推荐理由:Gemini Omni 把生成视频做成可对话编辑的对象,并同步在 Gemini App 等入口上线,读者可据此观察视频生成向可编辑素材演进。
@koraykv@koraykv精选AI 评分7676 
推荐理由:演示展示了 Gemini 3.5 Flash 在应用内生成分步搭建指南与可运行模拟,为交互式视觉学习提供了一个具体样例。
@minchoi@minchoi精选AI 评分8181
引用Logan Kilpatrick (@OfficialLoganK)@OfficialLoganKIntroducing Gemini Omni 🔮........ Omni is our new model that can create anything from any input — starting with video (think Nano Banana but for video). Available in the Gemini App, Flow, and YouTube, with API support coming soon! Video
推荐理由:原文给出 Gemini Omni 从任意输入生成视频的能力,以及 Gemini App、Flow 和 YouTube 的上线入口。