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Claude Opus 4.8 vs Claude Opus 5

vs

什麼情況選哪一個

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

領先高於平均沒有模型更高Claude Opus 4.8183 / 13819 / 138Claude Opus 52438 / 469 / 46

兩邊都有成績的有 25 項。

Claude Opus 4.8 Claude Opus 5 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
59%
68.8%
BioMysteryBench hard
42.4%
49.4%
OSWorld 2.0
55.7%
沒有模型分數更高 70.6%
ExploitBench (Cap%)
40%
70%
HealthBench Professional
57.4%
59.8%
Finance Agent v2
53.9%
58.6%
Legal Agent Benchmark
10.4%
6.7%
GPQA Diamond
92%
93.4%
Blueprint-Bench 2
14.5%
N/A
BrowseComp
84.3%
90.8%
LVBench
N/A
75.4%

供應商公布: 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 Opus 4.8 Claude Opus 5
工具使用 是 是
思考控制 可設定 可設定
結構化輸出 是 是
提示詞快取 顯式(由你標記前綴) 顯式(由你標記前綴)
快取存活時間 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 圖像 文字 圖像
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;供應商沒有公布的項目就直接略過,不自行推測。 完整來源: Claude Opus 4.8 · Claude Opus 5

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 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

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 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 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

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 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

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-4-8",
    # model="claude-opus-5",  # 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 4.8 和 Claude Opus 5 哪個比較便宜?

兩者的「輸入 / 1M tokens」相同($5),這一項分不出高下,請看下方的規格與功能。

可以只串接一次,就對 Claude Opus 4.8 和 Claude Opus 5 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 4.8 與 Claude Opus 5 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。

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