GPT-6 Astra：可雇佣的自动化AI工程师，时薪低于6美元

中文处理结果

GPT-6 Astra，OpenAI 推出的首个 Stargate 和轻量循环超模型，今日发布，在多项指标上明显优于 Fable 5.1，包括完全饱和最难的 FrontierMath（97.6%）和 ARC-AGI-3（99.9%）版本。许多演示将聚焦于典型的讨论话题，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 说这终于他想要的自动化 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，并将其应用于我们能想到的每一个实际、现实生活中的任务。在消耗了超过 200 亿个 Astra token 后，我们可以确认最令人惊讶的发现：GPT-6 Astra 是新型模型类别之一，它们本身就是完全有能力的 AI 工程师。它们现在可以帮助你选择和训练模型、标注数据（既帮助你标注，然后使用你的标签进行主动学习，如 SAM）、保持管道饱和、仪器化和读取日志、一次性部署和调试整个系统、扇出并指挥和评估子代理（包括运行其他模型的代理），并在单个代理线程的数十亿 token 上保持连贯性。

**提高你的抱负**

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，到构建了十几个内部/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性的 GitHub + Vercel 替代品，为战略棋盘游戏训练了游戏 AI，其合法移动次数比围棋多 10,000 倍，在个人财务清理中节省了数万美元，重新出版了我的旧书并同步了有声读物音频和印刷实体版，以及我们即将推出的更雄心勃勃的项目。

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 token，每百万 token 最高 50 美元。鉴于 Astra 比 Sol 和 Fable 更具 token 效率（由 Artificial Analysis 独立确认），这通常意味着 Astra 同时是你可以在 Spark 1.3 之外购买的最快且最智能的模型（假设我们的预览延迟在 GA 中保持不变）。

查看日志

管理子代理群（单独调整，有界并发）

当然，如果你在 Ultra 上直接使用 Astra，你每小时消耗的将远不止 6 美元……因为它非常擅长并行化。根据实际任务，我们经常同时启动 20-50 个代理，当然所有这些都由一个主 Astra 代理管理。

监控其自身的运行，启动和停止波次

原始正文

GPT-6 Astra: an automated AI Engineer you can hire for <$6 an hour

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.

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.

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.

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