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Claude Opus 5 vs DeepSeek V4 Flash (0731)

vs

什麼情況選哪一個

兩者皆具備 1,000,000 token 的 context window,因此差異在於模態、output 長度與價格:claude-opus-5 接受影像與文字 input,並允許您關閉其思考模式,而 deepseek-v4-flash-0731 僅支援文字,但允許高達 393,216 output token(對比 Opus 5 的 128,000)。在費率上,deepseek-v4-flash-0731 每百萬 input 為 $0.44、output 為 $1.32(對比 $5 與 $25),大約便宜 11x 與 19x。當影像或可切換的思考預算很重要時,請選擇 claude-opus-5;若處理需要極長生成的便宜、大量文字工作,請選擇 deepseek-v4-flash-0731。

Benchmark 成績

領先高於平均沒有模型更高Claude Opus 51138 / 468 / 46DeepSeek V4 Flash (0731)010 / 280 / 28

兩邊都有成績的有 11 項。

Claude Opus 5 DeepSeek V4 Flash (0731) 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
68.8%
54.4%
BioMysteryBench hard
49.4%
N/A
OSWorld 2.0
沒有模型分數更高 70.6%
N/A
ExploitGym
22.1%
1.8%
HealthBench Professional
59.8%
N/A
Finance Agent v2
58.6%
N/A
Legal Agent Benchmark
6.7%
N/A
GPQA Diamond
93.4%
89.9%
Agents' Last Exam
28.6%
25.2%
LVBench
75.4%
N/A

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

定價

Claude Opus 5 DeepSeek V4 Flash (0731) Δ
輸入 / 1M tokens $5 $0.44 11×
輸出 / 1M tokens $25 $1.32 19×
快取讀取 / 1M tokens $0.5 $0.044 11×
快取寫入 1.25x (5m) / 2x (1h) 不額外計費 -

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

Claude Opus 5 DeepSeek V4 Flash (0731)
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 顯式(由你標記前綴) 隱式(自動)
快取存活時間 5m default, 1h option no fixed TTL (evicted when unused)
最小快取前綴 1024 個 token 未公布

規格

Claude Opus 5 DeepSeek V4 Flash (0731)
輸入模態 文字 圖像 文字
輸出模態 文字 文字
發布日期 2026-07-24 2026-07-31
知識截止日期 2026-05 -
上下文視窗 1M 1M
最大輸出 128K 393K
思考參數
  • thinking.type
  • output_config.effort
-
可接受的值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
預設值

thinking on

effort
  • high (Claude API and Claude Code)
-

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: Claude Opus 5 · DeepSeek V4 Flash (0731)

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

Claude Opus 5 通過 · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

輸出 529 tok 延遲 9.0 s

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

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

Claude Opus 5 通過 · 8/8 cases

## Fixed version ```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 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

輸出 1094 tok 延遲 16.1 s

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

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

Claude Opus 5 通過 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

輸出 539 tok 延遲 7.6 s

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

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

Claude Opus 5 通過 · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

輸出 1593 tok 延遲 19.1 s

DeepSeek V4 Flash (0731) 未通過 · 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

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="claude-opus-5",
    # model="deepseek-v4-flash-0731",  # 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

Claude Opus 5 和 DeepSeek V4 Flash (0731) 哪個比較便宜?

以「輸入 / 1M tokens」來看,DeepSeek V4 Flash (0731) 比較便宜($0.44 對 $5,相差 11×)。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 Claude Opus 5 和 DeepSeek V4 Flash (0731) 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。

Claude Opus 5 與 DeepSeek V4 Flash (0731) 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