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Claude Opus 4.8 vs Claude Opus 5

vs

何時用哪一個

兩者費率完全相同:每百萬輸入 $5、輸出 $25、快取讀取 $0.5,上下文同為 1000000 token,單次最多輸出 128000 token,所以決定因素不是成本。claude-opus-5 從 512 token 起就能快取,而 claude-opus-4-8 需要 1024 token;前者預設開啟思考並提供 xhigh 與 max 兩檔 effort,後者預設關閉思考。升級改變的是行為與短前綴的快取命中率,不是帳單:預算照舊,但要重新檢查你固定過的 thinking 與 effort 設定。

Benchmark 成績

領先高於同儕均值無人分數更高Claude Opus 4.8082 / 12918 / 129Claude Opus 51414 / 147 / 14

雙方都被測過的 14 項。

Claude Opus 4.8 Claude Opus 5 其他被測模型 同儕均值 無人分數更高
DeepSWE 1.1
59%
68.8%
BioMysteryBench hard
42.4%
無人分數更高 49.4%
OSWorld 2.0
55.7%
無人分數更高 70.6%
Cybergym
78.3%
N/A
HealthBench Professional
57.4%
59.8%
GDPval-AA v2 Elo · 642-1861
1593
無人分數更高 1861
Legal Agent Benchmark held-out
10.4%
11.7%
Humanity's Last Exam no tools
49.8%
56.3%
Blueprint-Bench 2
14.5%
N/A
BrowseComp
84.3%
90.8%
Video-MME (w. sub)
86%
N/A

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

定價

Claude Opus 4.8 Claude Opus 5 Δ
輸入 / 1M tokens $5 $5 =
輸出 / 1M tokens $25 $25 =
快取讀取 / 1M tokens $0.5 $0.5 =
快取寫入 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h) -

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 64 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

能力

Claude Opus 4.8 Claude Opus 5
工具使用
思考控制 可配置 可配置
結構化輸出
提示快取 顯式(由您標記前綴) 顯式(由您標記前綴)
快取生命週期 5m default, 1h option 5m default, 1h option
最小快取前綴 1024 個 token 1024 個 token

規格

Claude Opus 4.8 Claude Opus 5
輸入模態 文字 影像 文字 影像
輸出模態 文字 文字
發布日期 2026-05-28 2026-07-24
知識截止日期 2026-01 2026-05
上下文視窗 1M 1M
最大輸出 128K 128K
思考參數
  • thinking.type "adaptive"
  • output_config.effort
  • thinking.type
  • output_config.effort
可接受的值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max

"enabled" returns 400

thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
預設值

thinking off

effort
  • high

thinking on

effort
  • high (Claude API and Claude Code)

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: Claude Opus 4.8 · Claude Opus 5

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

Claude Opus 4.8 通過 · 3 sentences

Reasoning models generate a hidden "thinking" phase before producing their final answer, and we're billed for every one of those internal tokens even though they never appear in the user-facing output. Because this deliberation often runs several times longer than the answer itself, our token consumption per request grew substantially even though our request volume stayed flat. The upside is that this trade buys us materially higher accuracy on complex tasks, so the right question isn't just cost but whether the improved output quality justifies the per-query premium for each use case.

輸出 162 tok 延遲 4.2 s

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

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

Claude Opus 4.8 通過 · 8/8 cases

## Fixed Function ```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 ``` ## Explanation of the Bug The original code counted **transitions between equal adjacent elements** rather than the **length of the run** itself. It initialized `best` and `cur` to `0` and incremented `cur` only when a match was found, so a run of length *N* recorded a count of *N-1* (e.g., a single element `[5]` never enters the loop and returns `0` instead of `1`, and `[5, 5]` returns `1` instead of `2`)—this is the off-by-one. […]

輸出 495 tok 延遲 9.2 s

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

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

Claude Opus 4.8 通過 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ```

輸出 72 tok 延遲 1.8 s

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

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

Claude Opus 4.8 通過 · 121 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching** Today we're launching prompt caching directly within our API gateway, designed to cut latency and reduce your inference costs. When identical or overlapping prompts hit the gateway, cached responses are served instantly instead of being routed to the underlying model provider again. What makes this different from provider-specific caching? Our system works across OpenAI, Anthropic, Google, and any custom endpoint you configure, so you maintain one consistent cache regardless of which model handles a request. You control cache duration, invalidation rules, and scope through simple configuration settings. Teams running high-volume workloads can expect meaningful savings on repeated queries and faster response times for end users. Cross-provider prompt caching is available now for all customers on paid plans.

輸出 258 tok 延遲 5.3 s

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

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="claude-opus-4-8",
    # model="claude-opus-5",  # 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

Claude Opus 4.8 和 Claude Opus 5 哪個比較便宜?

兩者列出的 輸入 / 1m tokens 相同($5),因此價格無法決定勝負 — 請參閱下方的規格與功能。

我可以在不進行兩次整合的情況下,對 Claude Opus 4.8 和 Claude Opus 5 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。

Claude Opus 4.8 與 Claude Opus 5 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