
Gemini 4 Carbon 與 DeepSeek V4 Pro:37 個指數點與每項任務 4.4 倍的成本,究竟實際換來什麼?
- Anthropic新Anthropic: Claude Fable 5.22026-11-09$8.00 / $40.00 每百萬 tokens · 100 tok/s
- orca新Orca: OrcaC2 1.02026-11-07235 tok/s
- Orca新OrcaCyber: GLM 5.3 Abliterated / Uncensored2026-11-04$3.00 / $5.00 每百萬 tokens · 119 tok/s
- Orca新OrcaCyber: Hy4 Abliterated / Uncensored2026-11-04$0.80 / $1.60 每百萬 tokens · 220 tok/s
- Orca新OrcaCyber: Kimi K3.1 Pro Abliterated / Uncensored2026-11-04$4.00 / $20.00 每百萬 tokens · 39 tok/s
- Orca新OrcaCyber: MiniMax M3.1 Flash Abliterated / Uncensored2026-10-29$0.80 / $1.60 每百萬 tokens · 122 tok/s
- OrcaOrcaCyber: GLM 5.3 Flash Abliterated / Uncensored2026-10-27$0.80 / $1.60 每百萬 tokens · 59 tok/s
- OrcaOrcaCyber: Qwen3.8 Flash Next Abliterated / Uncensored2026-10-26$0.80 / $1.60 每百萬 tokens · 96 tok/s
- OrcaOrcaCyber: MiMo V2.6 Flash Abliterated / Uncensored2026-10-26$0.80 / $1.60 每百萬 tokens · 40 tok/s
- MoonshotMoonshotAI: Kimi K3.1 Pro2026-10-2447智能
- OrcaOrcaCyber: DeepSeek V4.1 Flash Abliterated / Uncensored2026-10-23$0.80 / $1.60 每百萬 tokens · 114 tok/s
- minimaxMiniMax: M3.1 Flash2026-10-1929智能
- OrcaOrca: OrcaCyber Ultra 1.02026-10-17$5.00 / $10.00 每百萬 tokens · 103 tok/s
- deepseekDeepSeek: DeepSeek V4.1 Flash 10172026-10-1733智能
- DeepSeekDeepSeek: DeepSeek V4.1 Flash2026-10-1740智能
- OrcaOrca: OrcaCyber Zero 1.52026-10-10$3.00 / $7.50 每百萬 tokens · 41 tok/s
- openaiOpenAI: GPT-6.1 Sol2026-09-2955智能
- anthropicAnthropic: Claude Sonnet 5.52026-09-2855智能
- typesafeTypeSafe: Jev 1.132026-09-24$0.04 / $0.00 每百萬 tokens · 508 tok/s
- OpenAIOpenAI: GPT-6 Luna2026-09-2228智能
Put Gemini 4 Carbon and DeepSeek V4 Pro on the same scale and the result is not close: 60 against 23 on Artificial Analysis Intelligence Index v5.0.1, a 37-point gap between the fourth-best model on the board and one sitting below the median. Put them on the cost scale and it is not close either, in the other direction — $7.76 per index task against $1.77. The interesting question is not which is better. It is which of those two numbers you are actually paying for in the work you have, because for a large share of production traffic the 37 points go unused while the 4.4x bill arrives every month.
Both models are callable today, which makes this a real comparison rather than a roadmap exercise. Gemini 4 Carbon opened to paid API developers on 10 November 2026 after a twelve-day gated rollout. DeepSeek V4 Pro has been generally available since 13 August 2026 as the 0813 checkpoint, with open weights under MIT.
建立在相反假設之上的兩種模型
規格在任何基準測試之前,就已說明了大部分的行為差異。
• Price, standard rate — Gemini 4 Carbon: $2 in / $10 out per million until 30 November 2026, then $4 / $20. DeepSeek V4 Pro: $0.66 / $1.98 off-peak and $1.32 / $3.96 at peak, straight from DeepSeek's own rate card.
• Cached input — Carbon $0.10 per million now, $0.20 after 1 December, implicit caching only. DeepSeek $0.022 off-peak and $0.044 at peak, a 97% discount on the fresh-token rate.
• 上下文與輸出 — 兩者皆具備 1M-token 視窗。Carbon 將同步輸出上限設為 65,536 個 token,並允許在長解碼下最多達 1,048,576 個;V4 Pro 上限為 384,000。
• Input modality — Carbon takes text, image, audio, video and PDF. V4 Pro is text only, and DeepSeek's own pricing page says vision is not supported on v4-pro.
• 權重 — Carbon 是閉源的。V4 Pro 以 MIT 授權發佈,採用 1.6 兆參數的混合專家架構與 490 億個活躍參數,因此你可以自行託管、微調並在授權條款允許下重新發佈。
• 強度控制 — Carbon 提供低、中、高三種思考等級。V4 Pro 則提供低、高、最高三段推理強度調整。
模態這條界線不是註腳。若你的管線會攝入螢幕截圖、掃描版 PDF 或通話錄音,無論價格如何,V4 Pro 都不能成為堆疊中那一部分的模型,比較就只剩一個候選。若你的流量是程式碼、日誌和文字文件,那麼這個差距就真的是選擇問題。
37 個要點,逐一拆解
那麼大的綜合差距,通常是由少數幾項評測在起主要作用,而獨立評測排行榜對這一對模型所顯示的正是如此。Artificial Analysis 以高努力度測量 Carbon,並以最高努力度測量 V4 Pro 0813;這兩個數字都出自該評測機構,而非廠商。
• 每項 Intelligence Index 任務成本 — Carbon $7.76、V4 Pro $1.77。這個比率已將冗長程度納入考量,因此是兩者之間最公平的單一經濟數字。
• 在此指數上消耗的輸出 token——Carbon 2.05 億、V4 Pro 1.19 億,對比中位數 7,280 萬。兩款模型都很「多話」;Carbon 大約多出 1.7 倍,價格差距有一部分正是由此而來,而非僅來自每 token 費率。
• 速度與延遲 — Carbon 每秒輸出 {{1}}80{{/1}} 個 token,首個回答 token 需時 {{2}}66.85 秒{{/2}};V4 Pro 每秒 {{3}}67{{/3}} 個 token,需時 {{4}}67.71 秒{{/4}}。在這個面向上,兩者幾乎完全相同,而且兩者都慢到若沒有串流與耐心,都不適合放在互動式聊天流程中。
• 努力程度敏感度 — Carbon 在高強度下得分 60,中強度 58,低強度 54。V4 Pro 的非推理配置得分 13,而最高強度下為 23。當你把推理調低時,較便宜的模型損失遠更大,因此「便宜地跑」在兩邊都不是免費的切換。
On the vendor side the numbers are loud and unaudited, and they do not overlap enough to compare directly. DeepSeek's own card for the 0813 build reports Terminal-Bench 2.1 at 87.9, DeepSWE at 62.7, CyberGym at 83.3, SWE-bench Verified at 80.6 and a Codeforces rating of 3206 — all vendor-reported, none reproduced by a neutral party at the time of writing. Google's card for Carbon reports SEC-bench Pro at 88.2%, ExploitGym at 54.8% and CWE-bench at 71.0%. Note what happens if you try to line those up: CyberGym and ExploitGym are different harnesses, Terminal-Bench 2.1 and Terminal-Bench 4.0 are different versions of a moving target, and only one of the two vendors published a cyber score on a benchmark the other also ran. Any chart setting 83.3 beside 54.8 is comparing a scale to a different scale.

沒有人編列預算的計費時鐘
DeepSeek's price is not one price, and this is the most under-reported difference between the two models. V4 Pro bills at half rate outside a defined peak window: peak is 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday, excluding Chinese public holidays. Everything else — nights, weekends, holidays — is off-peak.
一週五天、每天二十四小時中有七小時,約占日曆週的 21%。如果你的批次工作是由 UTC 的 cron 排程,這很容易對齊。如果你的流量是具有美國或歐洲日間曲線的即時產品,其中相當比例會落在尖峰時段內,而你實際的混合費率會介於 $0.66 和 $1.32 之間,而不是行銷頁面上的數字。
Carbon 的計時方式正好相反。它在 11 月 30 日之前維持不變,之後則倍增。兩種模型,帳單差異的來源截然不同:一種隨一天中的時段變動,另一種隨日曆日期變動。這兩者都無法從單一的每 token 數字看出來;而拿 $4/$20 對比 $0.66/$1.98 的比較,描述的是一邊的最壞情況與另一邊的最好情況。
坦白說:以 Carbon 的 introductory rate(優惠初期費率)對比 V4 Pro 的 peak rate(高峰費率),Carbon 在輸入端是 1.5 倍,輸出端是 2.5 倍。12 月 1 日之後,對比 off-peak(離峰費率),則是 6 倍與 10 倍。每項任務 4.4 倍的成本數字正好落在這兩者之間,而這才是值得用來規劃的數字,因為它考慮了每個模型實際完成任務需要多少 token。

差距值得付出代價的地方
與其下定論,不如提出四項站得住腳的分野,並對照這兩個模型實際的形態來檢驗。
Cyber and vulnerability work. This is Carbon's home ground and the reason Google gated it to defenders first. Carbon reads code, images and PDFs in one call, which is what a vulnerability triage pipeline needs, and Google's own reported numbers put it at the top of the cyber boards it chose to publish. V4 Pro has respectable self-reported cyber scores and cannot see a screenshot. For a security team, Carbon at 6x the input price is not obviously the wrong call.
大量分類、擷取與路由。 把這個交給 V4 Pro。37 點綜合差距幾乎無法說明模型是否能標記客服工單,或從發票中擷取欄位;而每百萬輸入 $0.66 的便宜模型,早在品質上落敗之前,就先在算術上勝出。多數團隊在這裡都買過頭了。
長上下文文件處理。兩者的視窗都是 1M 個 token,因此在原始容量上打成平手。真正的差異在於模態與輸出上限:Carbon 可直接吃下掃描版 PDF,並輸出最多 1M 個 token;V4 Pro 則需要事先擷取好的文字,且上限只到 384K。如果你的流程已經完成 OCR,V4 Pro 以十分之一的輸出價格,提供相同的 1M 視窗。
任何你可能必須自行託管的東西。只有 V4 Pro 是選項。MIT 授權的權重意味著沒有供應商能撤回該模型、重新定價,或要求必須透過某項方案才能使用——這並非假設性的擔憂:在這一年,Carbon 的存取條件在十四天內變更了兩次,而其價格將於 12 月 1 日再次變動。
路由的答案,附上誠實的但書
這個決策的成熟做法不是二選一。而是讓 V4 Pro 承擔大部分流量,並將棘手案例升級到前沿模型,這正是我們的 routing DSL 存在的目的:一個負責處理流量的低成本分支、一個在置信度低或驗證失敗時觸發的升級條件,以及同時置於兩者前方的一個 OpenAI 相容端點。
What is on that endpoint today is deepseek/deepseek-v4-pro at DeepSeek's own $0.66/$1.98, alongside google/gemini-4-argon at $2/$10 — Argon being the Gemini 4 model that carries the same 1M window and a 50 on the same index, at half Carbon's post-December output price. Gemini 4 Carbon is not in our catalogue; it is reached through Google's paid API, and a Carbon branch in a pipeline is a direct integration. Because provider list price is passed through with 0% markup across the 200-plus models on the key, a vendor rate change lands the day it happens, which for Carbon means 1 December.
自動容錯移轉,正是改變平價模型成本盤算的關鍵。團隊之所以超額採購前沿等級的運算資源,是因為害怕在那 5% 真正重要的請求上得到糟糕的答案。有了容錯移轉與升級機制,平價模型就能接手它處理得游刃有餘的那 95%,而當某個請求超出它的能力範圍時,只要透過同一個端點就能触及昂貴的模型——不必為了對沖一段你其實可以改為路由的尾部風險,而在每個 token 上支付前沿等級的費率。

什麼會改變這項建議?
Two developments, both plausible within a quarter. If a neutral evaluator runs Carbon on a cyber benchmark that DeepSeek also published, the two scorecards become comparable for the first time and the gap stops being an inference. And if DeepSeek ships the V4.1-Pro that its own migration note pointed at, the cheap side of this comparison gets stronger without the expensive side moving — which would make the escalation split even more attractive than it is now.
在那之前,站得住腳的立場並不光鮮:以 V4 Pro 應付大量需求,只有在多模態輸入或網路能力具關鍵作用時才用 Carbon,而且不要在二十天內就會翻倍的價格兩端簽下長期合約。
