DeepSeek V4.1 Flash vs GPT-6 Luna

vs

什麼情況選哪一個

這兩者在紙面上涵蓋相同的領域:text 與 image 輸入、text 輸出,兩者皆具備 chat、vision、code、reasoning 與 tools 功能,因此差異主要在於費率表與 output 上限。gpt-6-luna 是較便宜的預設選擇,input 為 $0.1 且 output 為 $0.5,其 input 與 output 分別比 deepseek-v4.1-flash 便宜大約 3x 與 2.4x,並具有稍微較大的 1050000-token context 以及關閉 thinking 的選項。當單一回應需要較長篇幅時請選擇 deepseek-v4.1-flash,因為其 393216-token 的最大 output 大約是 gpt-6-luna 允許之 128000 的 3x。

Benchmark 成績

高於平均沒有模型更高DeepSeek V4.1 Flash14 / 194 / 19GPT-6 Luna僅 1 項可比
DeepSeek V4.1 Flash GPT-6 Luna 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
DeepSWE 1.1
沒有模型分數更高 74.2%
66.6%
Cybergym
沒有模型分數更高 88.1%
N/A
GPQA Diamond
90.9%
N/A
Agents' Last Exam
31.8%
N/A
Chartography with tools
78.9%
N/A

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

定價

DeepSeek V4.1 Flash GPT-6 Luna Δ
輸入 / 1M tokens $0.3 $0.1 3×
輸出 / 1M tokens $1.2 $0.5 2.4×
快取讀取 / 1M tokens $0.006 $0.01 0.6×
快取寫入 不額外計費 1.25x (30m) / 2.5x (12h) -

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 83 個聊天模型(對數尺度)

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

功能

DeepSeek V4.1 Flash GPT-6 Luna
工具使用 是 是
思考控制 是,但供應商未公布調整參數 可設定
結構化輸出 - 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) at least 30m by default, 12h option on the Responses API
最小快取前綴 未公布 1,024 個 token

規格

DeepSeek V4.1 Flash GPT-6 Luna
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-09-10 2026-09-22
知識截止日期 - 2026-05
上下文視窗 1M 1.1M
最大輸出 393K 128K
思考參數 - reasoning.effort
可接受的值 -
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
預設值 - 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 V4.1 Flash · GPT-6 Luna

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 V4.1 Flash 通過 · 3 sentences

Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.

輸出 423 tok (+327 思考) 延遲 14.6 s

GPT-6 Luna 通過 · 3 sentences

Enabling reasoning typically makes the model process more information and generate more internal tokens before producing its answer. Because usage is billed by tokens—and reasoning may also route requests to a more expensive model—the cost per request can rise sharply. If request volume stayed similar, that higher cost per request could explain why the bill doubled.

輸出 117 tok (+48 思考) 延遲 3.4 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 V4.1 Flash 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.

輸出 918 tok (+733 思考) 延遲 11.4 s

GPT-6 Luna 通過 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 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 ``` The original code counted matching *adjacent pairs* rather than items, so a run of length `n` was reported as `n - 1`, and a single-item input returned `0`. This version counts the current item as part of each run and updates `best` at every index, including the last; it returns `0` for an empty input.

輸出 514 tok (+356 思考) 延遲 6.9 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 V4.1 Flash 通過 · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}

輸出 1707 tok (+1667 思考) 延遲 15.1 s

GPT-6 Luna 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}

輸出 161 tok (+119 思考) 延遲 21.7 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 V4.1 Flash 通過 · 116 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.

輸出 917 tok (+770 思考) 延遲 9.0 s

GPT-6 Luna 通過 · 120 words, 0 banned, 1 question

Introducing Prompt Cache, a new API gateway feature that recognizes repeat prompt prefixes and reuses provider-side cached context across supported models. Teams can route requests to different AI providers while preserving eligible cache hits, reducing redundant input processing and helping lower latency and token costs. Configure cache policies in one place, monitor hit rates by provider, and keep existing client integrations unchanged. The gateway applies provider-specific rules automatically, so developers do not need to build separate caching logic for each endpoint. Which workflows could benefit from faster responses and predictable spend? Prompt Cache is available today in preview for eligible accounts, with usage details, supported providers, and setup guidance in the dashboard. Start with one route, compare results, then expand.

輸出 959 tok (+813 思考) 延遲 14.6 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-v4.1-flash",
    # model="gpt-6-luna",  # 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 V4.1 Flash 和 GPT-6 Luna 哪個比較便宜?

以「輸入 / 1M tokens」來看,GPT-6 Luna 比較便宜($0.1 對 $0.3,相差 3.0×)。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 V4.1 Flash 和 GPT-6 Luna 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 V4.1 Flash 與 GPT-6 Luna 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