DeepSeek V4.1 Flash vs GPT-6.1 Sol
何時用哪一個
這兩者的型態相近：皆接受文字與影像輸入並回傳文字，具備聊天、視覺、程式碼、推理及工具功能，且脈絡視窗幾乎相同，deepseek-v4.1-flash 為 1000000，而 gpt-6.1-sol 為 1050000。針對成本敏感或長篇幅的工作請選擇 deepseek-v4.1-flash：其輸入為 $0.3、輸出為 $1.2，比 gpt-6.1-sol 的 $2 與 $10 分別便宜約 6.7 倍與 8.3 倍，且其 393216 最大輸出約為 128000 上限的 3 倍。若您想要 OpenAI 較新的 2026-09-29 發行版本，請選擇 gpt-6.1-sol，需注意的是它的思考功能無法被關閉。
Benchmark 成績
GPT-6.1 Sol:廠商沒有公布過 benchmark 成績。
廠商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4.1 Flash | 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 | $0.03 | $0.1 | 0.3× |
| 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 76 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| DeepSeek V4.1 Flash | GPT-6.1 Sol | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是 —— 廠商未公布調節參數 | 常駐開啟 |
| 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-6.1 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 | 2026-09-29 |
| 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
|
| 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-6.1 Sol
單一提示詞，兩款模型 — 經由閘道測量
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-6.1 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
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。
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-6.1 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。
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-6.1 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
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。
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-6.1 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
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-6.1-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-6.1-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-6.1-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-6.1-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-6.1-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(""));常見問題
DeepSeek V4.1 Flash 和 GPT-6.1 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-6.1 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-6.1 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。