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DeepSeek V4 Pro vs Gemini 3.6 Flash

vs

什麼情況選哪一個

deepseek-v4-pro 在各項上都更便宜,只是攝入端優勢很小:輸入 $1.32 對 $1.5,輸出 $3.96 對 $7.5,快取讀取 $0.132 對 $0.15,而且單次回應最多可輸出 393216 token,對方為 65536。做純文字的長篇推理和程式碼選 deepseek-v4-pro,在 1000000 token 視窗內思考可隨時關閉。當輸入包含圖像、影片或音訊(音訊輸入每百萬 $5)、並且你想在 1048576 token 上用視覺加工具時,選 gemini-3.6-flash。

Benchmark 成績

高於平均沒有模型更高DeepSeek V4 Pro5 / 100 / 10Gemini 3.6 Flash6 / 191 / 19
DeepSeek V4 Pro Gemini 3.6 Flash 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
SWE-Bench Pro
59%
N/A
BioMysteryBench hard
N/A
41.2%
OSWorld 2.0
N/A
33.8%
GDPval-AA v2 Elo · 1422-1598 據 Google 公布 · 2026-08-25
N/A
1422
Harvey Lab-AA
N/A
85.1%
Humanity's Last Exam no tools
37.7%
N/A
AutomationBench
N/A
17%
LVBench
N/A
84.2%

供應商公布: Alibaba (Qwen) Anthropic DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

定價

DeepSeek V4 Pro Gemini 3.6 Flash Δ
輸入 / 1M tokens $1.32 $1.5 0.88×
輸出 / 1M tokens $3.96 $7.5 0.53×
快取讀取 / 1M tokens $0.132 $0.15 0.88×
快取寫入 不額外計費 - -

費率取自網站建置時的即時目錄;各模型頁面都列有最新的價目。

兩者的相對位置:每 1M tokens 的輸入價格,涵蓋同一計費單位下全部 76 個聊天模型(對數尺度)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

DeepSeek V4 Pro Gemini 3.6 Flash
工具使用 是 是
思考控制 可設定 一律開啟
結構化輸出 是 是
提示詞快取 隱式(自動) 隱式 + 顯式
快取存活時間 no fixed TTL (evicted when unused) 未公布
最小快取前綴 未公布 4096 個 token

規格

DeepSeek V4 Pro Gemini 3.6 Flash
輸入模態 文字 文字 圖像 音訊 影片
輸出模態 文字 文字
發布日期 2026-04-24 2026-07-21
知識截止日期 - 2026-03
上下文視窗 1M 1M
最大輸出 393K 66K
思考參數
  • thinking.type
  • reasoning_effort
thinkingLevel
可接受的值
thinking.type
  • enabled
  • disabled
reasoning_effort
  • high
  • max (low and medium map to high, xhigh maps to max)
  • minimal
  • low
  • medium
  • high
預設值

enabled, with reasoning_effort high

some complex agent requests are automatically set to max

medium

規格摘錄自各供應商的文件;供應商沒有公布的項目就直接略過,不自行推測。 完整來源: DeepSeek V4 Pro · Gemini 3.6 Flash

同一段提示詞,兩個模型,經閘道實測

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查 恰好 3 句

DeepSeek V4 Pro 通過 · 3 sentences

Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.

輸出 641 tok (+545 思考) 延遲 11.5 s

Gemini 3.6 Flash 通過 · 3 sentences

Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.

輸出 893 tok (+816 思考) 延遲 4.7 s

是否照指令做(剛好三句,數得出來)、語氣是否貼合對象(寫給 CFO 看),以及下方 token 計量顯示出的隱藏思考計費落差。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查 修正後通過測試

DeepSeek V4 Pro 通過 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 return best ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]

輸出 2418 tok (+2131 思考) 延遲 35.6 s

Gemini 3.6 Flash 通過 · 8/8 cases

Here is the fixed function along with an explanation of the bugs: ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` ### Explanation The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]

輸出 2034 tok (+1768 思考) 延遲 10.0 s

修正是否真的正確(跑得起來)、說明的資訊密度,以及在範圍明確的任務上的 token 效率。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查 有效的 JSON,完全符合 schema

DeepSeek V4 Pro 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }

輸出 1204 tok (+1153 思考) 延遲 19.9 s

Gemini 3.6 Flash 通過 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```

輸出 2843 tok (+2783 思考) 延遲 13.1 s

是否遵守 schema(不自創欄位)、面對幻覺誘因的表現(題目明確不給 guidance),以及結構化輸出路徑的差異。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞,0 個禁用詞

DeepSeek V4 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)

模型沒有回傳任何回答文字,token 額度全都用在隱藏的思考上了。

輸出 8193 tok (+8192 思考) 延遲 106.2 s

Gemini 3.6 Flash 未通過 · 60 words, 0 banned, 0 questions

72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]

輸出 4092 tok (+3929 思考) 延遲 16.3 s

是否遵守限制(字數上限、禁用詞清單、只能有一句問句)、文字風格的特徵,以及長度控制。

改一行程式碼就能在兩者之間切換

下面每個頁籤都列了這兩個模型 ID,要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    # model="gemini-3.6-flash",  # 取消這一行的註解,並把上一行註解掉
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

取得 API 金鑰 →

常見問題

DeepSeek V4 Pro 和 Gemini 3.6 Flash 哪個比較便宜?

以「輸入 / 1M tokens」來看,DeepSeek V4 Pro 比較便宜($1.32 對 $1.5,相差 1.1×)。其他項目的結果可能相反,完整價目請看上表;實際成本要看你的用量組合。

可以只串接一次,就對 DeepSeek V4 Pro 和 Gemini 3.6 Flash 做 A/B 測試嗎?

可以。兩個模型都走同一個 OpenAI 相容端點,用的也是同一把 API 金鑰,切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊,再直接比較帳單。

DeepSeek V4 Pro 與 Gemini 3.6 Flash 支援提示詞快取嗎?

支援。兩者的快取讀取費率都低於輸入費率,所以前綴已經進快取的工作負載,實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。

相關比較