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DeepSeek V4.1 Flash (1017) vs GPT-6.1 Sol

vs

什麼情況選哪一個

兩者的輸入輸出類型相同,皆接受文字與圖片輸入、以文字輸出,也都支援聊天、視覺、程式碼、推論與工具,上下文視窗分別為 1000000 和 1050000 個 token,因此取捨在於價格與輸出長度。deepseek-v4.1-flash-1017 的輸入費用為 $0.3,gpt-6.1-sol 為 $2,約為前者的 6.7 倍;輸出費用分別為 $1.2 和 $10,後者約為前者的 8.3 倍。deepseek-v4.1-flash-1017 的輸出上限為 393216 個 token,對方為 128000,因此適合大量處理或產生長篇內容的工作。若需要結構化輸出或批次請求,請選擇 gpt-6.1-sol,因為 deepseek-v4.1-flash-1017 並未列出這些功能;若需要明確標示的知識截止日期,gpt-6.1-sol 標示為 2026-04。

定價

DeepSeek V4.1 Flash (1017) GPT-6.1 Sol Δ
輸入 / 1M tokens $0.3 $2 0.15×
輸出 / 1M tokens $1.2 $10 0.12×
快取讀取 / 1M tokens $0.006 $0.1 0.06×
快取寫入 不額外計費 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 79 個聊天模型(對數尺度)

功能

DeepSeek V4.1 Flash (1017) GPT-6.1 Sol
工具使用 是 是
思考控制 是,但供應商未公布調整參數 一律開啟
結構化輸出 - 是
提示詞快取 隱式(自動) 隱式(自動)
快取存活時間 no fixed TTL (evicted when unused) 5-10m, up to 1h
最小快取前綴 未公布 1024 個 token

規格

DeepSeek V4.1 Flash (1017) GPT-6.1 Sol
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-10-17 2026-09-29
知識截止日期 - 2026-04
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
  • 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 (1017) · GPT-6.1 Sol

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 (1017) 通過 · 3 sentences

Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.

輸出 253 tok (+178 思考) 延遲 5.4 s

GPT-6.1 Sol 通過 · 3 sentences

Enabling reasoning makes the model do more computational work before answering, and many providers bill the resulting reasoning tokens even though users never see them. That means the same number of requests can cost twice as much because each request consumes more billable tokens, even if the visible answers stay the same length. We should confirm the increase in our usage breakdown and reserve higher reasoning levels for tasks where the improvement justifies the added cost.

輸出 144 tok (+54 思考) 延遲 6.6 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 (1017) 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 bug is that `cur` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.

輸出 1300 tok (+1095 思考) 延遲 11.8 s

GPT-6.1 Sol 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 function counts equal adjacent pairs rather than items in a run: a run of length `n` has only `n - 1` equal pairs, so a single-element input returns `0`, and a run ending at the last index is also undercounted. It does process the last index, so the issue is not a missing final check. Initializing each nonempty run to `1` and updating `best` after every iteration correctly counts items, including singleton and trailing runs; empty inputs still return `0`.

輸出 189 tok 延遲 6.3 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 (1017) 通過 · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}

輸出 1375 tok (+1337 思考) 延遲 12.2 s

GPT-6.1 Sol 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"

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

輸出 345 tok (+285 思考) 延遲 10.2 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 (1017) 通過 · 120 words, 0 banned, 1 question

Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.

輸出 2487 tok (+2335 思考) 延遲 17.1 s

GPT-6.1 Sol 通過 · 120 words, 0 banned, 1 question

Introducing Cross-Provider Prompt Cache, a new API gateway feature that stores reusable prompts and manages caching across your supported AI providers. Why rebuild the same context every time your application switches models? With one configuration, teams can reuse shared instructions, standardize cache policies, and reduce repeated prompt processing wherever provider caching is available. The gateway handles provider-specific requirements while giving you clear visibility into cache hits, usage, and estimated savings. Set expiration windows, isolate cached content by project, and invalidate entries when prompts change. Your existing routing logic stays intact, so you can compare models without rebuilding your caching workflow. […]

輸出 588 tok (+435 思考) 延遲 13.9 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-1017",
    # model="gpt-6.1-sol",  # 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)

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常見問題

DeepSeek V4.1 Flash (1017) 和 GPT-6.1 Sol 哪個比較便宜?

以「輸入 / 1M tokens」來看,DeepSeek V4.1 Flash (1017) 比較便宜($0.3 對 $2,相差 6.7×)。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 (1017) 和 GPT-6.1 Sol 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 (1017) 與 GPT-6.1 Sol 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。

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