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DeepSeek V4 Pro (0813) vs Qwen3.8 Flash

vs

什麼情況選哪一個

兩款模型皆具備 1000000 token 的上下文視窗,並涵蓋聊天、程式碼、推理與工具,因此真正的差異在於價格、輸出長度與輸入:deepseek-v4-pro-0813 每百萬輸入收取 $1.32,每百萬輸出收取 $3.96,大約是 qwen3.8-flash($0.15 與 $0.47)的 8.8x 與 8.4x。當您需要極長的單次回覆時(高達 393216 token 對比 131072),請選擇 deepseek-v4-pro-0813。對於大量的文字工作、圖像與影片輸入,或者當您想在較簡單的呼叫中停用思考時,請選擇 qwen3.8-flash。

Benchmark 成績

高於平均沒有模型更高DeepSeek V4 Pro (0813)13 / 211 / 21Qwen3.8 Flash12 / 163 / 16
DeepSeek V4 Pro (0813) Qwen3.8 Flash 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
DeepSWE 1.1
62.7%
58.7%
OSWorld 2.0 partial
N/A
52.3%
Cybergym
83.3%
N/A
JobBench
N/A
55.7%
GPQA Diamond
92.4%
91.7%
ERQA
N/A
沒有模型分數更高 72.3%
Agents' Last Exam
25.7%
N/A
LVBench
N/A
76.6%

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

定價

DeepSeek V4 Pro (0813) Qwen3.8 Flash Δ
輸入 / 1M tokens $1.32 $0.15 8.8×
輸出 / 1M tokens $3.96 $0.47 8.4×
快取讀取 / 1M tokens $0.132 $0.016 8.3×
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 77 個聊天模型(對數尺度)

功能

DeepSeek V4 Pro (0813) Qwen3.8 Flash
工具使用 是 是
思考控制 一律開啟 可設定
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 (0813) Qwen3.8 Flash
輸入模態 文字 文字 圖像 影片
輸出模態 文字 文字
發布日期 2026-08-13 2026-08-27
上下文視窗 1M 1M
最大輸出 393K 131K
思考參數 reasoning_effort
  • enable_thinking
  • thinking_budget
  • preserve_thinking
可接受的值
reasoning_effort
  • the model card documents low
  • high
  • max
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
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 Pro (0813) · Qwen3.8 Flash

同一段提示詞,兩個模型,經閘道實測

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 Pro (0813) 通過 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

輸出 308 tok (+226 思考) 延遲 5.1 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。

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 修正後通過測試

DeepSeek V4 Pro (0813) 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` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

輸出 1315 tok (+1130 思考) 延遲 16.9 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。

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 有效的 JSON,完全符合 schema

DeepSeek V4 Pro (0813) 通過 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

輸出 2155 tok (+2121 思考) 延遲 26.0 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。

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 Pro (0813) 通過 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

輸出 2845 tok (+2694 思考) 延遲 25.5 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-pro-0813",
    # 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)

取得 API 金鑰 →

常見問題

DeepSeek V4 Pro (0813) 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 $1.32,相差 8.8×)。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 (0813) 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 Pro (0813) 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。

相關比較