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大模型 latent.space ·

GPT-6 Astra:每小时成本低于6美元的自动化AI工程师

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Latent Space团队使用超过200亿token对GPT-6 Astra进行了全面测试,分享了关于这一自动化AI工程师的实践经验与见解。文章可能涵盖其能力、局限和适用场景。

AI推荐理由 Latent Space对GPT-6 Astra进行了深入探索,声称花费200亿+token,提供实际学习经验。对AI工程师和研究者有高价值,但摘要简短,需查看原文获取细节。

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GPT-6 Astra:一个每小时费用低于 6 美元的自动化 AI 工程师 我们花费了超过 200 亿个 GPT-6 Astra 代币来探索一切。以下是我们的经验。 2026 年 9 月 3 日 120 12 分享 GPT-6 Astra,OpenAI 推出的首个 Stargate 和轻度循环超级模型,今天发布,在许多指标上轻松击败 Fable 5.1,包括完全饱和最难的 FrontierMath 版本(97.6%)和 ARC-AGI-3(99.9%)。许多演示将集中在典型的宣传话题上,如计算机使用、Pokemon 游戏、Blender、科学和网络安全基准(系统卡)。Greg 说 AGI 已经到来,Jakub 说它终于成为他想要的自动化 AI 研究实习生。 我们没有资格谈论这些,但我们获得了早期访问权限,并将其投入到我们能想到的每一个实际、现实任务中。在烧掉超过 200 亿个 Astra 代币后,我们可以确认最令人惊讶的发现:GPT-6 Astra 是一类新模型之一,它们本身就是完全有能力的 AI 工程师。它们现在帮助您选择和训练模型、标记数据(既帮助您标记,然后使用您的标签进行主动学习,如 SAM)、保持管道饱和、检测和读取日志、一次性部署和调试整个系统、扇出并命令和评估子代理(包括运行其他模型的代理),并在单个代理线程的数十亿代币中保持连贯性。 提高您的期望 我们之前写过关于提高对 LLM 期望的高回报活动。我们的经验使我们比以往任何时候都更加雄心勃勃。在过去的一个月里,我们从提示人类进行有趣的“Kill My SaaS”竞赛,到构建十几个内部/个人工具,包括 4 个以前付费的 SaaS 工具,完全重新设计了我的个人网站,制作了一个不完整但功能正常的 GitHub + Vercel 替代品,为具有比围棋多 10,000 倍合法移动的策略棋盘游戏训练了游戏 AI,节省了数万美元的个人财务清理费用,重新出版了我的旧书并配有同步有声读物音频和印刷实体版,以及更多我们即将推出的雄心勃勃的项目。 每小时 6 美元的数字可能听起来令人惊讶,但这正是我们在测试中看到的——每秒 33 个代币,最高每百万代币 50 美元。鉴于 Astra 比 Sol 和 Fable 更节省代币(由 Artificial Analysis 独立确认),这通常意味着 Astra 同时是您可以购买的最好的快速智能模型(假设我们的预览延迟在 GA 中保持不变),除了 Spark 1.3。 查看日志 管理子代理群(单独调整,有界并发) 当然,如果您在 Ultra 上使用 Astra,您每小时会烧掉超过 6 美元……因为它非常擅长并行化。根据任务的不同,我们经常同时启动 20-50 个代理,当然都由一个主 Astra 代理管理。 监控自己的运行,启动和停止波次 这基本上就是您会付钱给初级 AI 工程师做的事情——照看运行、盯着数据、发现问题、修复、重新运行,如此循环。您可以以每天 200-1000 美元雇佣某人,或者您可以以 100 美元雇佣 GPT-6 两天来做这件事。 制作模型基准、处理预算、进行估计、扩展运行、获得人类评级 因为当然您需要所有这些能力来运行自己的 AI 工程计划,因为当然 OpenAI 已经在内部使用 GPT-6 来做这件事…… 示例在此 或者您可以让 Astra 轻松地为您制作自己的个人 Arena.ai 克隆,用于调整提示、为您的任务选择模型或对齐您自己的偏好模型! 您应该得出的总体结论是,OpenAI 显然训练了一个能够自动化其自身大部分 AI 工程的模型,现在是时候学习利用 Astra 和 Fable 类模型,并对您对代理的期望更加不合理了。 1 我们正在对 Grok、Fable 和其他类似的边界模型进行类似工作,但 OpenAI 在试用限制方面最慷慨,所以这得到了报道——但这里讨论的代理编码模式可能适用于所有此类 2026 年末的边界模型。 2 你们中的许多人正在等待结果……抱歉沉默!我们……忙了!我们将宣布获胜者、报销和最佳尝试。 120 12 分享 上一篇下一篇
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GPT-6 Astra: an automated AI Engineer you can hire for <$6 an hour We spent 20B+ tokens of GPT-6 Astra to explore everything. Here’s our learnings. Sep 03, 2026 120 12 Share GPT-6 Astra, the first Stargate and lightly looped supermodel from OpenAI, launched today, cleanly beating Fable 5.1 on many metrics including completely saturating the hardest versions of FrontierMath (97.6%) and ARC-AGI-3 (99.9%). Lots of demos will focus on typical talk tracks like the computer use to the Pokemon playing to Blender to the scientific and cybersafety benchmarks (system card). Greg says AGI is here, and Jakub says it is finally the Automated AI Research Intern he wanted. We aren’t qualified to talk about those, but we got early access and threw it at every practical, real-life task we could think of. After burning over 20B tokens of Astra, we can confirm the most surprising finding: GPT-6 Astra is one of a new class of models1 that are fully capable AI Engineers in their own right. They now help you choose and train models, label data (both helping you label and then using your labels for active learning, like SAM), keep pipelines saturated, instrument and read logs, deploy and debug entire systems in one shot, fan out and command and eval subagents (including agents running other models), and keep coherence over billions of tokens of a single agent thread. Raising Your Ambitions We’ve written before about the high-return activity of raising your aspirations for LLMs. Our experience has made us exponentially more ambitious than we have ever been. Over the past month, we went from prompting humans for a fun “Kill My SaaS” competition2, to building a dozen internal/personal tools, including 4 previously paid SaaS tools, fully redesigned my personal site, made an incomplete but functional replacement of GitHub + Vercel, trained game AI for a strategy board game with 10,000x more legal moves than Go, saved tens of thousands of dollars in personal finance cleanups, republished my old book with synced audiobook audio and printed physical editions, and even more ambitious projects we will launch soon. The $6 an hour number might sound surprising, but that’s exactly what we saw in our testing - 33 tokens per second at a max $50 per million token rate. Given that Astra is more token efficient than Sol and Fable (independently confirmed by Artificial Analysis), it often means that Astra is simultaneously also the best fast-and-smart model you can buy (assuming our preview latency holds for GA), outside of Spark 1.3. see logs Managing fleets of subagents (individually tweaked, bounded concurrency) Now of course, if you just throw on Astra at Ultra you’re gonna burn through a lot more than $6 per hour…. because it is so dang good at parallelizing. Depending on the task in practice we were often ramping up between 20-50 agents in parallel, of course all managed by one main Astra agent. Monitoring its own runs, starting and stopping waves This is basically what you would pay a junior AI Engineer to do — babysitting runs, staring at data, finding issues, fixing, rerunning, ad infinitum. You could hire someone at $200-$1000 a day, or you can hire GPT-6 for $100 over 2 days to do this. Making model benchmarks, handling budgets, making estimates, scaling up runs, getting human ratings Because of course you need all these capabilities to run your own AI engineering program, because of course OpenAI already uses GPT-6 to do this internally… example here Or you can get Astra to trivially whip up your own personal Arena.ai clone for tuning your prompts, picking models for your task, or aligning yrou own preference model! The overall conclusion you should have is that OpenAI have clearly trained a model that is capable of automating much of their own AI Engineering, and it is finally time that you learn to exploit Astra- and Fable-class models and be far, far more unreasonable with your own expectations of what you can do with agents now. 1 We are running similar work on Grok, Fable and other similar frontier models but OpenAI was most generous with trial limits so this gets the writeup - but the agentic coding patterns discussed here will likely apply to all such late 2026 frontier models. 2 Many of you are waiting to hear results… sorry for the radio silence! we got… busy! We will announce winners and reimbursements and best attempts. 120 12 Share PreviousNext

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