新用戶 免費註冊,送 10 次呼叫,最高 $1,免綁卡。

DeepSeek V4.1 Flash vs Qwen3.8 Max

vs

何時用哪一個

這兩者在事實上有高度重疊:皆接受文字和影像輸入,回傳文字,並涵蓋對話、視覺、程式碼、推理和工具,具備幾乎相同的上下文視窗(deepseek-v4.1-flash 為 1000000,qwen3.8-max 為 983616)。區別在於成本和輸出空間 —— deepseek-v4.1-flash 的輸入為 $0.3,輸出為 $1.2,對比對手的 $2 和 $6,大約便宜 6.7x 和 5x,快取讀取為 $0.03 對比 $0.25,並允許最大 393216 的輸出 tokens 對比 131072。單從這些事實來看,除了留在你已經運作的模型上之外,沒有什麼理由偏好 qwen3.8-max;對於新工作,deepseek-v4.1-flash 能以更低的成本滿足相同的使用情境。

Benchmark 成績

領先高於同儕均值無人分數更高DeepSeek V4.1 Flash816 / 194 / 19Qwen3.8 Max230 / 398 / 39

雙方都被測過的 10 項。

DeepSeek V4.1 Flash Qwen3.8 Max 其他被測模型 同儕均值 無人分數更高
NL2Repo
64%
55.9%
AndroidBench
N/A
75.1%
Cybergym
無人分數更高 88.1%
78.5%
HealthBench
N/A
無人分數更高 60.2%
GDPval-AA v2 Elo · 1508-1769 據 Z.ai 公布 · 2026-09-04
N/A
1739
PLawBench
N/A
無人分數更高 73.2%
GPQA Diamond
90.9%
92.6%
Agents' Last Exam
31.8%
27%
BabyVision with tools
89.6%
N/A

廠商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

定價

DeepSeek V4.1 Flash Qwen3.8 Max Δ
輸入 / 1M tokens $0.3 $2 0.15×
輸出 / 1M tokens $1.2 $6 0.2×
快取讀取 / 1M tokens $0.03 $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 69 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

能力

DeepSeek V4.1 Flash Qwen3.8 Max
工具使用
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 -
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 V4.1 Flash Qwen3.8 Max
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 2026-08-03
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 V4.1 Flash · Qwen3.8 Max

單一提示詞,兩款模型 — 經由閘道測量

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 V4.1 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 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 V4.1 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 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 V4.1 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 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 V4.1 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 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-v4.1-flash",
    # 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)

取得 API 金鑰 →

常見問題

DeepSeek V4.1 Flash 和 Qwen3.8 Max 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 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 V4.1 Flash 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。

相關比較