DeepSeek V4.1 Flash vs Qwen3.8 Flash
什麼情況選哪一個
兩款模型皆具備 1,000,000 token 的上下文視窗、文字加圖像輸入,以及相同的聊天、視覺、程式碼、推理和工具標記,因此差異在於成本與輸出上限。qwen3.8-flash 的輸入價格只有一半(每百萬 $0.15 對比 $0.3),輸出大約便宜 2.6x($0.47 對比 $1.2),同時接受影片,帶有長上下文標記並允許您關閉思考,這使其成為大量或多模態攝取工作的預設選擇。當您需要比 Qwen 限制更長的單次回覆時,請選擇 deepseek-v4.1-flash:最大輸出為 393,216 token 對比 131,072,空間大約是 3x。
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 | Qwen3.8 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.3 | $0.15 | 2× |
| 輸出 / 1M tokens | $1.2 | $0.47 | 2.6× |
| 快取讀取 / 1M tokens | $0.006 | $0.016 | 0.38× |
| 快取寫入 | 不額外計費 | 1.25x | - |
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 80 個聊天模型(對數尺度)
功能
| DeepSeek V4.1 Flash | Qwen3.8 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是,但供應商未公布調整參數 | 可設定 |
| 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) | explicit: 5m, reset on hit |
| 最小快取前綴 | 未公布 | 1,024 個 token |
規格
| DeepSeek V4.1 Flash | Qwen3.8 Flash | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 圖像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-10 | 2026-08-27 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 393K | 131K |
| 思考參數 | - |
|
| 可接受的值 | - | enable_thinking
thinking_budget
preserve_thinking
|
| 預設值 | - | on qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off |
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 · Qwen3.8 Flash
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
Qwen3.8 Flash 通過 · 3 sentences
Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.
輸出 330 tok (+262 思考) 延遲 8.9 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
Qwen3.8 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 the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.
輸出 2123 tok (+1933 思考) 延遲 43.8 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
Qwen3.8 Flash 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
輸出 519 tok (+463 思考) 延遲 10.5 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
Qwen3.8 Flash 通過 · 120 words, 0 banned, 1 question
We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.
輸出 5958 tok (+5805 思考) 延遲 88.8 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="qwen3.8-flash", # 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: "qwen3.8-flash", // 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": "qwen3.8-flash", # 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: "qwen3.8-flash", // 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("qwen3.8-flash") // 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 Qwen3.8 Flash 哪個比較便宜?
以「輸入 / 1M tokens」來看,Qwen3.8 Flash 比較便宜($0.15 對 $0.3,相差 2.0×)。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 Qwen3.8 Flash 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 Qwen3.8 Flash 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。