DeepSeek V4 Pro vs Qwen3.8 Max
什麼情況選哪一個
大批量純文字工作選 deepseek-v4-pro:每百萬 token 輸入更便宜($1.32 對 $2),輸出也更便宜($3.96 對 $6,約 1.5 倍),快取讀取 $0.132 對 $0.25,並且允許最多 393216 輸出 token,對 131072——思考還能關閉。需要圖像輸入時選 qwen3.8-max,它接受文字和圖像,而 deepseek-v4-pro 是文字進文字出。上下文基本打平,1000000 對 983616 token,兩者都涵蓋聊天、程式碼、推理和工具。
Benchmark 成績
供應商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4 Pro | Qwen3.8 Max | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $1.32 | $2 | 0.66× |
| 輸出 / 1M tokens | $3.96 | $6 | 0.66× |
| 快取讀取 / 1M tokens | $0.132 | $0.25 | 0.53× |
| 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 76 個聊天模型(對數尺度)
功能
| DeepSeek V4 Pro | Qwen3.8 Max | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 是,但供應商未公布調整參數 |
| 結構化輸出 | 是 | 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 | 1024 個 token |
規格
| DeepSeek V4 Pro | Qwen3.8 Max | |
|---|---|---|
| 輸入模態 | 文字 | 文字 圖像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-04-24 | 2026-08-03 |
| 上下文視窗 | 1M | 984K |
| 最大輸出 | 393K | 131K |
| 思考參數 |
| - |
| 可接受的值 | thinking.type
reasoning_effort
| - |
| 預設值 | enabled, with reasoning_effort high some complex agent requests are automatically set to max | - |
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 Pro · Qwen3.8 Max
同一段提示詞,兩個模型,經閘道實測
DeepSeek V4 Pro 通過 · 3 sentences
Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.
輸出 641 tok (+545 思考) 延遲 11.5 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
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 Pro 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 ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]
輸出 2418 tok (+2131 思考) 延遲 35.6 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 Pro 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
輸出 1204 tok (+1153 思考) 延遲 19.9 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
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 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)
模型沒有回傳任何回答文字,token 額度全都用在隱藏的思考上了。
輸出 8193 tok (+8192 思考) 延遲 106.2 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
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-pro",
# model="qwen3.8-max", # 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-pro",
// model: "qwen3.8-max", // 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-pro",
# "model": "qwen3.8-max", # 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-pro",
// Model: "qwen3.8-max", // 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-pro")
// .model("qwen3.8-max") // 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 Pro 和 Qwen3.8 Max 哪個比較便宜?
以「輸入 / 1M tokens」來看,DeepSeek V4 Pro 比較便宜($1.32 對 $2,相差 1.5×)。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 Pro 和 Qwen3.8 Max 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 Pro 與 Qwen3.8 Max 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。