GPT-6 Astra vs GPT-6.1 Sol vs Gemini 4 Argon vs Claude Fable 5.1: Which Frontier Model Fits Which Job
https://www.marktechpost.com/2026/10/04/gpt-6-astra-vs-gpt-6-1-sol-vs-gemini-4-argon-vs-claude-fable-5-1-which-frontier-model-fits-which-job/📌 【OpenAI、Anthropic、Google DeepMind 30天內三箭齊發】四款前沿模型,到底該用哪一個?
TL;DR:GPT-6 Astra、GPT-6.1 Sol、Gemini 4 Argon、Claude Fable 5.1 的價格與實測成本差異,比跑分排名更該先看。
9月28日,OpenAI 悄悄取消了 GPT-6.1 Astra。原因不是跑分不夠看,而是這款模型在內部的「範圍與授權」測試中失敗。這件事值得玩味:在短短30天內,Anthropic、OpenAI、Google DeepMind 一共端出4款前沿模型,卻有一款在上桌前就被自家公司撤下。剩下的4款模型跑分看起來高度重疊,但價格、存取權限與實際任務成本的差距,遠比發布文宣透露得更大。
🤔 四款模型,發布時間只隔不到一個月
Claude Fable 5.1 於9月1日上線,GPT-6 Astra緊接在9月3日發布,GPT-6.1 Sol與Gemini 4 Argon則在9月底相繼登場。GPT-6 Astra目前仍是OpenAI的旗艦模型(GPT-6.1 Astra被取消後的現狀)。
📊 價格差到5倍,快取讀取價差更誇張
以標準第一方牌價、短context層級來看:
| 模型 | 開發機構 | Input/Output(每百萬token列表價) |
|---|---|---|
| Claude Fable 5.1 | Anthropic | $10/$50 |
| GPT-6 Astra | OpenAI | $10/$50 |
| GPT-6.1 Sol | OpenAI | 約為前兩者的1/5 |
| Gemini 4 Argon | Google DeepMind | 約為前兩者的1/5(屬早鳥優惠價,之後會翻倍) |
對agent應用而言,「快取輸入」這一行價格更關鍵,因為agent每一步都要重送system prompt、工具schema與對話歷史。文章指出,Astra的快取讀取價為每百萬token $1.00,是Fable 5.1的4倍、Sol的10倍——換算下來Fable 5.1約$0.25、Sol約$0.10。另外,Argon的輸出上限達1M token,是結構上唯一的例外,其餘三款都卡在128K token/次回應。
💡 跑分沒有誰全面稱霸
根據Google DeepMind公布的比較表,Argon在知識型工作與長程coding任務上領先;Astra在前沿軟體工程與computer use任務上領先;而不在這次比較名單內的Opus 5.5,則在Terminal-Bench 4.0上稱王。GPT-6.1 Sol沒有出現在Google的比較表中,但OpenAI自家數據顯示其表現接近Astra。
獨立評測機構Artificial Analysis的數字則呈現另一面向:在其Intelligence Index上,Astra得61分,Fable 5.1高出5分;在coding-agent指標上,Fable 5.1在Claude Code環境下拿到70分,Astra為67分;Argon則在Intelligence Index上與Astra同分。在ARC-AGI-2上,Astra以95%領先Fable 5.1的90%。
成本面上,Artificial Analysis算出Fable 5.1平均每項任務成本為$9.18,Astra為$4.72,約為1.9倍,而兩者列表價完全相同——差距來自token用量而非單價。Anthropic也坦承其較新的tokenizer在相同文字下會多產生約30%的token量。
⚠️ 快取重度使用時,排名可能反過來
上述「每任務成本」沒有把重度快取重用的情境算進去。長時間運行的agent每一步都在重讀同一份context。文章以200K token的快取context為例做試算(純粹是基於列表快取價率的示意計算,不含輸出token):Astra的200K context仍低於其272K的長prompt門檻;但在快取密集的迴圈中,Fable 5.1讀取context的費率只有Astra的四分之一。這代表若你的agent架構是「context重用多、一次性輸出少」,Fable 5.1在成本上反而可能更划算,建議用自己的實際trace去量測,而不是只看單一任務成本的頭條數字。
存取權限也是現實考量:Argon目前只開放給「Fairwind」網路防禦計畫使用;Astra完整的攻擊性安全能力被鎖在OpenAI的Daybreak計畫後面,公開版本會拒絕進階的攻擊性網路任務;Anthropic的無限制版本Claude Mythos 5.1,也同樣鎖在受信任存取計畫之後。
值得留意的是,這些數字大多是廠商自報,彼此之間也會有落差——例如OpenAI自報Astra在Terminal-Bench 4.0上為57.9%,但Google的比較表列出的卻是58.2%。
🎯 先看工作型態,再看排行榜名次
對大多數團隊來說,GPT-6.1 Sol的性價比是目前最務實的預設選擇;其餘三款則是在特定場景(前沿軟體工程/computer use、長時間agent的快取密集任務、知識型與長程coding工作)才值得付出溢價。選型前務必用自己的真實使用模式(尤其是context重用比例)去換算成本,而不是只比對單一跑分或單一任務成本headline。
🔗 來源
- 標題:GPT-6 Astra vs GPT-6.1 Sol vs Gemini 4 Argon vs Claude Fable 5.1: Which Frontier Model Fits Which Job
- 作者/機構:Asif Razzaq, MarkTechPost
- 連結:https://www.marktechpost.com/2026/10/04/gpt-6-astra-vs-gpt-6-1-sol-vs-gemini-4-argon-vs-claude-fable-5-1-which-frontier-model-fits-which-job/
#GPT6 #ClaudeFable #Gemini4 #FrontierAI #OpenAI #Anthropic #GoogleDeepMind #LLMPricing #AIBenchmark #AgentCost
原始資料 MarkTechPost · 收集於 2026-10-05
摘要原文
Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 after it failed internal scope and authorization tests. GPT-6 Astra stays OpenAI’s top model for now. Astra and Fable 5.1 share the same $10 input and $50 output list price. Sol and Argon list at one-fifth of that. Argon’s price is introductory and doubles later. Sources: OpenAI GPT-6.1 Sol coverage , Gemini 4 Argon coverage , GPT-6 Astra long-context pricing , Claude Fable 5.1 specs . Standard first-party list prices, short-context tier. The cached-input row matters most for agents. Agents resend system prompts, tool schemas and history on every step. Astra’s $1.00 cache read is 4x Fable 5.1’s and 10x Sol’s. Argon’s 1M output cap is the only structural outlier. The other 3 stop at 128K tokens per response. No model sweeps the board. Argon leads the knowledge-work and long-horizon coding rows. Astra leads frontier software engineering and computer use. Opus 5.5, not in this lineup, leads Terminal-Bench 4.0. Source: Google DeepMind’s published comparison , as reported in our Gemini 4 Argon coverage . Vendor-reported. GPT-6.1 Sol is not in Google’s table. OpenAI’s own numbers place it close to Astra: Independent signals point the other way on raw intelligence. On the Artificial Analysis Intelligence Index, Astra scores 61. Fable 5.1 scores 5 points higher. On its coding-agent index, Fable 5.1 in Claude Code scores 70 against Astra’s 67. Artificial Analysis also reports that Argon equals Astra on the Intelligence Index. On ARC-AGI-2, Astra scores 95% and Fable 5.1 scores 90%. Artificial Analysis puts Claude Fable 5.1 at $9.18 per task, against $4.72 for GPT-6 Astra. That is about 1.9x, at identical list prices. The gap comes from token volume, not rates. Cost per task multiplies price by tokens spent. With equal rates, the gap implies Fable 5.1 spent more tokens per task in that run. Anthropic also notes its newer tokenizer produces roughly 30% more tokens for the same text. The two cheaper models change the picture further: Caching can reverse the ranking for agents. The per-task figures above do not model heavy cache reuse. A long-running agent rereads the same context on every step. Here is the arithmetic for a 200K-token cached context, before output tokens: Illustrative math from list cache rates. Astra’s 200K context stays under its 272K long-prompt threshold. In cache-heavy loops, Fable 5.1 reads context at a quarter of Astra’s rate. Measure both on your own traces before you pick on per-task headlines. Pick by workload, not by leaderboard rank. GPT-6.1 Sol is the default for most teams. The other 3 earn their price on narrower jobs. Access decides 2 of these rows. Argon is only available to Fairwind cyber defenders today. Astra’s full offensive-security capability sits behind OpenAI’s Daybreak program; the public release refuses advanced offensive cyber tasks. Anthropic gates its unrestricted twin, Claude Mythos 5.1, behind trusted access programs. Most numbers here are vendor-reported, and vendors disagree at the margins. OpenAI reports Astra at 57.9% on Terminal-Bench 4.0. Google’s comparison table lists 58.2%. The post GPT-6 Astra vs GPT-6.1 Sol vs Gemini 4 Argon vs Claude Fable 5.1: Which Frontier Model Fits Which Job appeared first on MarkTechPost .
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