DeepSeek V4.1 Flash vs Gemini 4 Argon
什麼情況選哪一個
兩者的上下文長度都大約落在一百萬個 token (deepseek-v4.1-flash 為 1000000，gemini-4-argon 為 1048576)，因此差異在於價格與輸入類型：deepseek-v4.1-flash 的輸入收費為 $0.3、輸出為 $1.2，相對於 gemini-4-argon 的 $2 與 $10，這讓 gemini-4-argon 的價格分別貴了約 6.7 倍 和 8.3 倍；此外，前者支援 393216 個輸出 token，而後者僅有 65536。如果是大批量的圖文任務、長篇生成，或需要 $0.006 快取讀取的場景，請選擇 deepseek-v4.1-flash。如果需要音訊或視訊輸入 (deepseek-v4.1-flash 不支援此類輸入)，則選擇 gemini-4-argon。
Benchmark 成績
Gemini 4 Argon：供應商沒有公布 benchmark 成績。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4.1 Flash | Gemini 4 Argon | 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 | - | - |
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 | Gemini 4 Argon | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是，但供應商未公布調整參數 | 是，但供應商未公布調整參數 |
| 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) | 未公布 |
| 最小快取前綴 | 未公布 | 4096 個 token |
規格
| DeepSeek V4.1 Flash | Gemini 4 Argon | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 圖像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-10 | 2026-10-13 |
| 知識截止日期 | - | 2026-03 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 393K | 66K |
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 · Gemini 4 Argon
同一段提示詞，兩個模型，經閘道實測
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
Gemini 4 Argon 通過 · 3 sentences
Enabling the reasoning feature forces the AI to generate an extensive, hidden "chain of thought" to solve complex problems before delivering the final answer. Because LLM providers bill us per token generated, we are now paying for thousands of these invisible "thinking" words on top of the standard visible output. While this doubles our raw usage costs per query, it represents an investment in significantly higher accuracy and drastically reduced error rates for our most critical tasks.
輸出 91 tok 延遲 4.7 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。
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
Gemini 4 Argon 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 best = max(best, cur) else: cur = 1 return best ``` The bug occurs because the original function counts the number of matching adjacent *pairs* rather than the actual number of *elements* in a run, causing all non-empty results to be off by one. Because a single element constitutes a valid run of length 1, initializing and resetting `cur` and `best` to `0` instead of `1` means the first element of any sequence is never counted; this causes single-element lists (where the loop doesn't run) to wrongly return `0` and makes it seem like runs at the end are missed because a pair of matching elements only increments `cur` to `1` instead of `2`. […]
輸出 286 tok 延遲 7.5 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。
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
Gemini 4 Argon 通過 · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
輸出 68 tok 延遲 5.8 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。
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
Gemini 4 Argon 通過 · 120 words, 0 banned, 1 question
Are you paying twice for the exact same AI responses? Today, we are introducing Cross-Provider Prompt Caching for our API gateway, a new feature designed to reduce your artificial intelligence infrastructure costs and latency. When your applications send identical prompts to different large language models, our gateway now intercepts the request and serves a verified, cached response in milliseconds. It works across OpenAI, Anthropic, and open-source models simultaneously. If your primary provider experiences an outage, your fallback model still benefits from the shared cache Layer, preventing expensive redundant computations. Engineering teams can configure custom time-to-live settings, set similarity thresholds, and monitor cache hit rates directly from the dashboard. Activate Cross-Provider Prompt Caching in your gateway settings today and start saving.
輸出 156 tok 延遲 6.3 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="gemini-4-argon", # 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)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: "gemini-4-argon", // 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",
});
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": "gemini-4-argon", # 取消這一行的註解，並把上一行註解掉
"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: "gemini-4-argon", // 取消這一行的註解，並把上一行註解掉
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("gemini-4-argon") // 取消這一行的註解，並把上一行註解掉
.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 Gemini 4 Argon 哪個比較便宜？
以「輸入 / 1M tokens」來看，DeepSeek V4.1 Flash 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 和 Gemini 4 Argon 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 Gemini 4 Argon 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。