DeepSeek V4.1 Flash 對比 GPT-6 Sol:價格與規格實測

Q: DeepSeek V4.1 Flash 和 GPT-6 Sol 哪個比較便宜？

DeepSeek V4.1 Flash 在 輸入 / 1m tokens 上較便宜（$0.3 對比 $2，相差 6.7×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。

何時用哪一個

兩者皆接受文字和影像輸入並回傳文字，具備相同的聊天、視覺、程式碼、推理及工具標籤，且上下文長度幾乎相同（deepseek-v4.1-flash 為 1,000,000，而 gpt-6-sol 為 1,050,000）。對於大量需求與長生成任務，請選擇 deepseek-v4.1-flash：輸入便宜約 6.7x（$0.3 對 $2），輸出便宜約 8.3x（$1.2 對 $10），快取讀取為 $0.03 對 $0.2，且最大輸出 token 數為 393216 對 128000。當您希望在每次請求中能關閉推理功能，或者需要考量標示為 2026-04 的知識截止日期來進行規劃時，請選擇 gpt-6-sol。

Benchmark 成績

高於同儕均值無人分數更高DeepSeek V4.1 Flash14 ~~/ 19~~4 ~~/ 19~~GPT-6 Sol僅 4 項可比

DeepSeek V4.1 Flash GPT-6 Sol 其他被測模型同儕均值 ★ 無人分數更高

DeepSWE 1.1

無人分數更高 74.2%

68.8%

OSWorld 2.0 offline set, partial

N/A

60.5%

Cybergym

無人分數更高 88.1%

N/A

GPQA Diamond

90.9%

N/A

Agents' Last Exam

31.8%

56.4%

Chartography with tools

78.9%

N/A

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

定價

	DeepSeek V4.1 Flash	GPT-6 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	$0.03	$0.2	0.15×
快取寫入	不額外計費	不額外計費	-

費率取自建置時的即時目錄；各模型頁面皆附有目前的費率卡。

它們的相對位置 — 在此計費單位下，所有 74 個聊天模型的每 1M tokens 的輸入價格（對數尺度）

DeepSeek V4.1 Flash · $0.3 GPT-6 Sol · $2

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

能力

	DeepSeek V4.1 Flash	GPT-6 Sol
工具使用	是	是
思考控制	是 —— 廠商未公布調節參數	可配置
結構化輸出	-	是
提示快取	隱式（自動）	隱式（自動）
快取生命週期	no fixed TTL (evicted when unused)	5-10m, up to 1h
最小快取前綴	未公開	1024 個 token

規格

	DeepSeek V4.1 Flash	GPT-6 Sol
輸入模態	文字影像	文字影像
輸出模態	文字	文字
發布日期	2026-09-10	2026-09-22
知識截止日期	-	2026-04
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 none low medium high xhigh max
預設值	-	medium

規格摘錄自各供應商的文件；供應商未發布的資料列會直接省略，而非自行推測。完整來源： DeepSeek V4.1 Flash · GPT-6 Sol

單一提示詞，兩款模型 — 經由閘道測量

針對 CFO 的推論修復包含錯誤的函式擷取至 schema 依照限制條件撰寫

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 Sol 通過 · 3 sentences

Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.

輸出 188 tok (+111 思考) 延遲 5.0 s

指令遵循（恰好三句，可數）、受眾適配（面向 CFO 的語氣），以及下方 token 計量所暴露的隱藏思考計費缺口。

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. 檢查修復通過測試

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 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 `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.

輸出 206 tok (+59 思考) 延遲 5.9 s

修復是否真的正確（可執行）、解釋的資訊密度，以及在一個邊界明確的任務上的 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. 檢查合法 JSON，schema 精確

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

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

輸出 227 tok (+185 思考) 延遲 5.2 s

schema 服從度（不臆造欄位）、幻覺壓力（guidance 明確被暫緩給出），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 Sol 通過 · 120 words, 0 banned, 1 question

Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.

輸出 586 tok (+443 思考) 延遲 7.7 s

約束服從度（字數預算、禁用詞表、唯一的那句問句）、文風指紋，以及長度控制。

只需一行程式碼即可在兩者間切換

以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點，相同的金鑰，相同的請求結構。

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-sol",  # 取消註解此行，並註解上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://synthorai.io/v1",
  apiKey: "sk-syn-...",
});

const resp = await client.chat.completions.create({
  model: "deepseek-v4.1-flash",
  // model: "gpt-6-sol",  // 取消註解此行，並註解上一行
  messages: [{ role: "user", content: "Summarize this diff" }],
  reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);

curl https://synthorai.io/v1/chat/completions \
  -H "Authorization: Bearer sk-syn-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4.1-flash",
    # "model": "gpt-6-sol",  # 取消註解此行，並註解上一行
    "messages": [{"role": "user", "content": "Hello"}],
    "reasoning_effort": "medium"
  }'

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://synthorai.io/v1"),
		option.WithAPIKey("sk-syn-..."),
	)
	resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
		Model: "deepseek-v4.1-flash",
		// Model: "gpt-6-sol",  // 取消註解此行，並註解上一行
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Summarize this diff"),
		},
		ReasoningEffort: openai.ReasoningEffortMedium,
	})
	fmt.Println(resp.Choices[0].Message.Content)
}

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://synthorai.io/v1")
    .apiKey("sk-syn-...")
    .build();

ChatCompletion resp = client.chat().completions().create(
    ChatCompletionCreateParams.builder()
        .model("deepseek-v4.1-flash")
        // .model("gpt-6-sol")  // 取消註解此行，並註解上一行
        .addUserMessage("Summarize this diff")
        .reasoningEffort(ReasoningEffort.MEDIUM)
        .build());
System.out.println(resp.choices().get(0).message().content().orElse(""));

取得 API 金鑰 →

常見問題

DeepSeek V4.1 Flash 和 GPT-6 Sol 哪個比較便宜？

DeepSeek V4.1 Flash 在輸入 / 1m tokens 上較便宜（$0.3 對比 $2，相差 6.7×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。

我可以在不進行兩次整合的情況下，對 DeepSeek V4.1 Flash 和 GPT-6 Sol 進行 A/B 測試嗎？

可以。兩者皆透過同一個相容 OpenAI 的端點提供服務，並使用同一把 API 金鑰 — 切換只需更改一行的模型字串，因此您可以將部分流量分別導向兩者並直接比較帳單。

DeepSeek V4.1 Flash 與 GPT-6 Sol 支援提示快取嗎？

是的 — 兩者的快取讀取費率皆低於其輸入費率，因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。

DeepSeek V4.1 Flash vs GPT-6 Sol

Benchmark 成績

定價

能力

規格

單一提示詞，兩款模型 — 經由閘道測量

只需一行程式碼即可在兩者間切換

常見問題

相關比較