新帳號 免費註冊,送 10 次呼叫,最高 $1,免綁卡。

Claude Haiku 5.5 vs DeepSeek V4.1 Flash

vs

什麼情況選哪一個

兩款都是平價模型,上下文視窗同為 1000000 個 token,都接受文字與圖片輸入。以每個 token 計,claude-haiku-5-5 比較便宜,輸入 $0.1、輸出 $0.5,對比 $0.3 與 $1.2,快取讀取則是 $0.01 對比 $0.03;但它的提示詞超過 100000 個 token 後按 5 倍計費,而 deepseek-v4.1-flash 只有一個費率,到了這個區間反而比較便宜。deepseek-v4.1-flash 允許的回應也長得多:可輸出 393216 個 token,對比 128000。大量的短提示詞選 claude-haiku-5-5,長提示詞或長輸出選 deepseek-v4.1-flash。

Benchmark 成績

Claude Haiku 5.5:供應商沒有公布 benchmark 成績。

高於平均沒有模型更高DeepSeek V4.1 Flash14 / 194 / 19
Claude Haiku 5.5 DeepSeek V4.1 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
N/A
沒有模型分數更高 74.2%
Cybergym
N/A
沒有模型分數更高 88.1%
GPQA Diamond
N/A
90.9%
Agents' Last Exam
N/A
31.8%
Chartography with tools
N/A
78.9%

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

定價

Claude Haiku 5.5 DeepSeek V4.1 Flash Δ
輸入 / 1M tokens $0.1 $0.3 0.33×
輸出 / 1M tokens $0.5 $1.2 0.42×
快取讀取 / 1M tokens $0.01 $0.03 0.33×
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 78 個聊天模型(對數尺度)

功能

Claude Haiku 5.5 DeepSeek V4.1 Flash
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 -
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 Haiku 5.5 DeepSeek V4.1 Flash
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-10-07 2026-09-10
知識截止日期 2026-06 -
上下文視窗 1M 1M
最大輸出 128K 393K
思考參數 output_config.effort -
可接受的值
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
-
預設值 medium -

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 Haiku 5.5 · DeepSeek V4.1 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

Claude Haiku 5.5 通過 · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

輸出 332 tok (+159 思考) 延遲 2.9 s

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

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 Haiku 5.5 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 `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

輸出 1012 tok (+591 思考) 延遲 6.0 s

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

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 Haiku 5.5 通過 · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

輸出 725 tok (+572 思考) 延遲 4.6 s

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

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 Haiku 5.5 通過 · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

輸出 2164 tok (+1929 思考) 延遲 9.9 s

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

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-haiku-5-5",
    # model="deepseek-v4.1-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

Claude Haiku 5.5 和 DeepSeek V4.1 Flash 哪個比較便宜?

以「輸入 / 1M tokens」來看,Claude Haiku 5.5 比較便宜($0.1 對 $0.3,相差 3.0×)。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 Haiku 5.5 和 DeepSeek V4.1 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。

Claude Haiku 5.5 與 DeepSeek V4.1 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