GPT-6 Astra 对 Fable 5.1 十五个真实任务实测
15 个真实用例中 Astra 赢 10 局，总成本 326.98 美元低于对手 513.36 美元，但总耗时多约 1 小时 43 分，胜在会先追问澄清。
ran GPT-6 Astra against Fable 5.1 across 15 real use cases, here's what actually separates them Astra won 10, Fable won 5 where Fable wins: - consulting deck: more presentable, structured, columns, consistent branding, even though Astra was less than half the cost and 14 minutes faster - sales letter copy: answered more of the actual buyer objections, "will this get me hired," "am I technical enough," Astra's version felt more high-level in comparison - HTML explainer doc: more screenshots, more annotation, a better learning experience for an actual beginner, even though Astra's version was cheaper - brain visualization UI: simpler, cleaner, easier to navigate, Astra's interface felt more overwhelming here - website cloning from a URL: closer to the original site's feel, Astra's version had layout bugs and got stuck scrolling where Astra wins: - taxes: asked 7 clarifying questions upfront before touching the task, more tailored output, thousands of transaction rows tracked, cost more and took longer but the confidence it built was worth it - email subscription audit: Fable left one entire sheet blank, Astra's was complete and cost $12 vs Fable's $50 - meeting analysis: reviewed 79 meetings vs Fable's 58, more specific pain points identified, again cheaper - event recap video, sizzle reel, game dev, SaaS eval app: won on polish, energy, or interface cleanliness, usually cheaper too - browser-driven course upload: Astra handled video upload correctly, Fable failed outright - YouTube 12-month strategy review: more visual, easier to read, but cost more and took longer here the overall tally: Astra total cost $326.98 across all 15, Fable $513.36, Astra took about 1h43m longer in total runtime the pattern that holds across most wins: Astra asks more clarifying questions before starting, which shows up as better-tailored output on data-heavy or judgment-heavy tasks Fable tends to run immediately, which wins on speed and sometimes on presentation polish for anything client-facing or visually driven neither model wins everything, and the takeaway isn't "switch entirely," it's matching the model to the specific type of deliverable
这条是引用着另一条写的：x.com/rewind02/status/2099105999713923498