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DeepSeek V4 Pro (0813) vs GPT-6.1 Sol

vs

什麼情況選哪一個

兩者皆為輸出文字的推理模型,支援工具與程式碼,且脈絡視窗約為一百萬個 token(deepseek-v4-pro-0813 為 1,000,000,而 gpt-6.1-sol 為 1,050,000),因此真正的差異在於價格、輸出長度與影像輸入。若預算有限且需要生成長文,請選擇 deepseek-v4-pro-0813:輸入為 $1.32 且輸出為 $3.96,在輸出方面比定價 $10 的 gpt-6.1-sol 便宜約 2.5 倍,且最大輸出為 393,216 個 token,優於後者的 128,000。當您需要影像輸入或其略微便宜的 $0.1 快取讀取時,請選擇輸入價格為 $2 的 gpt-6.1-sol。

Benchmark 成績

GPT-6.1 Sol:供應商沒有公布 benchmark 成績。

高於平均沒有模型更高DeepSeek V4 Pro (0813)13 / 211 / 21
DeepSeek V4 Pro (0813) GPT-6.1 Sol 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
62.7%
N/A
Cybergym
83.3%
N/A
GDPval-AA v2 Elo · 1508-1769 據 Z.ai 公布 · 2026-09-04
1590
N/A
GPQA Diamond
92.4%
N/A
Agents' Last Exam
25.7%
N/A

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

定價

DeepSeek V4 Pro (0813) GPT-6.1 Sol Δ
輸入 / 1M tokens $1.32 $2 0.66×
輸出 / 1M tokens $3.96 $10 0.4×
快取讀取 / 1M tokens $0.132 $0.1 1.3×
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 77 個聊天模型(對數尺度)

功能

DeepSeek V4 Pro (0813) GPT-6.1 Sol
工具使用 是 是
思考控制 一律開啟 一律開啟
結構化輸出 是 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 Pro (0813) GPT-6.1 Sol
輸入模態 文字 文字 圖像
輸出模態 文字 文字
發布日期 2026-08-13 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 reasoning.effort
可接受的值
reasoning_effort
  • the model card documents low
  • high
  • max
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 Pro (0813) · 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 Pro (0813) 通過 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

輸出 308 tok (+226 思考) 延遲 5.1 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 Pro (0813) 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` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

輸出 1315 tok (+1130 思考) 延遲 16.9 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 Pro (0813) 通過 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

輸出 2155 tok (+2121 思考) 延遲 26.0 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 Pro (0813) 通過 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

輸出 2845 tok (+2694 思考) 延遲 25.5 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-pro-0813",
    # 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 Pro (0813) 和 GPT-6.1 Sol 哪個比較便宜?

以「輸入 / 1M tokens」來看,DeepSeek V4 Pro (0813) 比較便宜($1.32 對 $2,相差 1.5×)。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 (0813) 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 Pro (0813) 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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