GPT-6 Astra評測：貴2.5倍，電腦操作實測有感 | GPT-6 Astra Review: 2.5x Pricier, Real Computer-Use Gains

By Kit 小克 | AI Tool Observer | 2026-09-07

🇹🇼 GPT-6 Astra評測：貴2.5倍，電腦操作實測有感

GPT-6 Astra 是 OpenAI 於 2026 年 9 月 3 日推出的最新旗艦模型，號稱是史上訓練規模最大的一次——動用超過 10 萬張 GPU、在德州 Stargate 廠區訓練，而且是第一個「由上一代模型監督訓練」的版本。跳過 GPT-5.7 直接叫 Astra，官方說法是「AGI 時代」的起點，但實際用起來到底值不值得，數字比行銷詞更誠實。

GPT-6 Astra 跑分：哪些是真的突破

先看硬指標。GPT-6 Astra 在 FrontierMath Tier 4 拿下 97.6%、ARC-AGI-3 衝到 99.9%，兩者都接近「跑分被打爆」的程度。長文本記憶測試 MRCR v2 更誇張：512K–1M token 範圍內準確率 96.3%，前代 GPT-5.6 Sol 只有 73.8%，代表 1.1M token 的超長上下文終於不是「塞得進去但記不住」的擺設。

真正有感的是電腦操作能力。OSWorld V2-Offline（測試橫跨多個桌面應用程式完成任務）分數從 Sol 的 65.7% 提升到 72.6%，平均每個任務耗時從 75 分鐘壓縮到 40 分鐘。早期試用者反應也印證這點：有人直接靠 Astra 操作 Unreal Engine 做出可玩的 3D 遊戲場景，也有人拿它自動化整套業務流程。這不是跑分作弊，是真的能少盯螢幕。

寫程式進步有限，Meta 反而更猛

但別被 AGI 這個詞唬到。在 DeepSWE v1.1 這個 113 題的真實代理編碼測試中，Astra 拿 74.1%，只比 Sol 的 70.8% 進步一點點；同期 Meta 的 Muse Spark 1.3 在最高推理設定下反而拿到 75.4%，寫程式這件事 OpenAI 並沒有明顯領先。程式碼審查方面倒是有實質進步，比 Sol 多抓出約 4% 的已知 bug，比 Anthropic Opus 5 多抓 22%，跨檔案的複雜審查場景差距更大。

代價：貴 2.5 倍，還有安全隱憂

API 定價是每百萬 input token 10 美元、output token 50 美元，比 Sol 貴了約 2.5 倍。這筆帳只有在 Astra 真的減少你反覆修正、重跑的次數時才划算——單純把舊 prompt 換模型直接跑，帳單會很難看。另一個該注意的數字是 ExploitBench 拿到 100%，代表這個模型寫攻擊腳本、找漏洞的能力已經到頂格，企業導入前該把紅隊測試和權限控管排進待辦清單，而不是只看它能幫你多快交付功能。

該不該換？

如果你的工作痛點是長文件理解、跨應用程式的電腦操作自動化，GPT-6 Astra 的進步是實打實的，值得升級。如果你只是拿它寫程式，Sol 或 Meta 的 Muse Spark 1.3 可能性價比更好，沒必要為了「Astra」這個名字多付 2.5 倍。好不好用，試了才知道。

🇺🇸 GPT-6 Astra Review: 2.5x Pricier, Real Computer-Use Gains

GPT-6 Astra, OpenAI's newest flagship model launched September 3, 2026, is being called the company's largest training run ever — over 100,000 GPUs at the Stargate site in Texas, and the first release where an earlier OpenAI model supervised the training of its successor. OpenAI skipped straight past a 5.7 label and framed this as the start of the "AGI era." The benchmarks, not the marketing copy, tell you whether that's earned.

What GPT-6 Astra Actually Improves

The headline numbers are real: 97.6% on FrontierMath Tier 4, 99.9% on ARC-AGI-3 — both near-saturated. The long-context test MRCR v2 is more telling: at the 512K–1M token range, Astra holds 96.3% accuracy versus just 73.8% for predecessor GPT-5.6 Sol, meaning the 1.1M-token context window is finally usable rather than decorative.

The gain people actually feel is computer-use capability. On OSWorld V2-Offline, which tests multi-app desktop task completion, Astra scored 72.6% versus Sol's 65.7%, while cutting average time per task from 75 minutes to 40. Early testers back this up — one built a playable 3D game environment in Unreal Engine entirely through Astra, others automated whole business workflows. That's not a benchmark trick; it's fewer hours staring at a screen.

Coding Gains Are Modest — Meta Is Right There

Don't let AGI fool you on coding. On DeepSWE v1.1, a 113-task agentic coding benchmark, Astra scored 74.1%, barely ahead of Sol's 70.8%. Meta's Muse Spark 1.3, released the same week, hit 75.4% at its top reasoning setting — OpenAI has no clear lead here. Code review is the one area with a real edge: Astra catches roughly 4% more known bugs than Sol and 22% more than Anthropic's Opus 5, with the gap widening on complex cross-file reviews.

The Cost: 2.5x Price, and a Security Red Flag

API pricing is per million input tokens and per million output tokens — about 2.5x Sol's cost. That premium only pays off if Astra genuinely cuts your retry/debug cycles; swapping models on the same old prompts will just inflate your bill. Also worth flagging: Astra hit 100% on ExploitBench, meaning its ability to write exploit code and find vulnerabilities is essentially maxed out. Enterprises adopting it should schedule red-teaming and access controls, not just measure shipping speed.

Should You Switch?

If your bottleneck is long-document understanding or automating multi-app desktop work, GPT-6 Astra's gains are real and worth the upgrade. If you mainly use it for coding, Sol or Meta's Muse Spark 1.3 may deliver better value — there's no reason to pay 2.5x just for the Astra name. 好不好用，試了才知道 (you won't know until you try it).

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