DeepSeek V4 Flash (0731) vs Qwen3.8 Max

Q: DeepSeek V4 Flash (0731) 和 Qwen3.8 Max 哪個比較便宜？

DeepSeek V4 Flash (0731) 在 輸入 / 1m tokens 上較便宜（$0.308 對比 $2，相差 6.5×）。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。

更換任一側 vs

何時該用哪一個 — 綜合評斷，而非基準測試數據表

兩者皆為純文字輸入/輸出的推理與工具模型，且上下文規模大致相同——deepseek-v4-flash-0731 為 1000000 tokens，而 qwen3.8-max 為 983616 tokens——因此真正的差異在於價格與模態。若要處理高流量的文字工作，請選擇 deepseek-v4-flash-0731：其輸入（$0.308 對 $2）與輸出（$0.924 對 $6）便宜約 6.5x，快取讀取便宜約 8x，且允許 393216 輸出 tokens（對比於 131072）。當您需要圖片輸入或其長上下文（long-context）標記，且能承受較高的定價時，請選擇 qwen3.8-max。

定價

	DeepSeek V4 Flash (0731)	Qwen3.8 Max	Δ
輸入 / 1M tokens	$0.308	$2	0.15×
輸出 / 1M tokens	$0.924	$6	0.15×
快取讀取 / 1M tokens	$0.0308	$0.25	0.12×
快取寫入	不額外計費	1.25x	—

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 63 個聊天模型的每 1M tokens 的輸入價格（對數尺度）

DeepSeek V4 Flash (0731) · $0.308 Qwen3.8 Max · $2

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

能力

	DeepSeek V4 Flash (0731)	Qwen3.8 Max
工具使用	是	是
思考控制	是 —— 廠商未公布調節參數	是 —— 廠商未公布調節參數
結構化輸出	是	是
提示快取	隱式（自動）	隱式 + 顯式
快取生命週期	no fixed TTL (evicted when unused)	explicit: 5m, reset on hit
最小快取前綴	未公開	1024 個 token

規格

	DeepSeek V4 Flash (0731)	Qwen3.8 Max
輸入模態	文字	文字影像
輸出模態	文字	文字
發布日期	2026-07-31	2026-08-03
上下文視窗	1M	984K
最大輸出	393K	131K

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 Flash (0731) · Qwen3.8 Max

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 Flash (0731) 通過 · 3 sentences

Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.

輸出 365 tok (+266 思考) 延遲 5.1 s

Qwen3.8 Max 通過 · 3 sentences

Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.

輸出 378 tok (+305 思考) 延遲 8.6 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。

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 Flash (0731) 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 original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]

輸出 1268 tok (+1016 思考) 延遲 14.0 s

Qwen3.8 Max 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 bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.

輸出 1616 tok (+1411 思考) 延遲 34.7 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 Flash (0731) 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }

輸出 1119 tok (+1072 思考) 延遲 12.3 s

Qwen3.8 Max 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

輸出 1199 tok (+1141 思考) 延遲 24.4 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 Flash (0731) 未命中 · 141 words, 0 banned, 1 question

**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]

輸出 254 tok (+80 思考) 延遲 4.4 s

Qwen3.8 Max 通過 · 120 words, 0 banned, 1 question

Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.

輸出 2744 tok (+2591 思考) 延遲 46.3 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-flash-0731",
    # model="qwen3.8-max",  # 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-flash-0731",
  // model: "qwen3.8-max",  // 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-flash-0731",
    # "model": "qwen3.8-max",  # 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-flash-0731",
		// Model: "qwen3.8-max",  // 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-flash-0731")
        // .model("qwen3.8-max")  // 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 Flash (0731) 和 Qwen3.8 Max 哪個比較便宜？

DeepSeek V4 Flash (0731) 在輸入 / 1m tokens 上較便宜（$0.308 對比 $2，相差 6.5×）。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 Flash (0731) 和 Qwen3.8 Max 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 Flash (0731) 與 Qwen3.8 Max 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。

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