Claude Opus 5 vs Claude Opus 5.5
何時用哪一個
這兩款 Anthropic 模型在通常決定選擇的規格上一致：兩者皆接受文字與圖片輸入、輸出文字、具備 1000000-token 的上下文與 128000 的最大輸出，並列出相同的對話、程式碼、思考、工具及推理功能。claude-opus-5-5 是較新的版本（2026-09-22 釋出，知識截止日為 2026-06），也是較便宜的選擇，輸入 $4 與輸出 $20 相對應於 $5 與 $25，快取讀取為 $0.2 對 $0.5，便宜了 2.5 倍。只有當您需要關閉思考功能時才選擇 claude-opus-5，因為它允許這麼做而 claude-opus-5-5 則否；否則請選擇 5.5。
Benchmark 成績
雙方都被測過的 9 項。
廠商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Claude Opus 5 | Claude Opus 5.5 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $5 | $4 | 1.3× |
| 輸出 / 1M tokens | $25 | $20 | 1.3× |
| 快取讀取 / 1M tokens | $0.5 | $0.2 | 2.5× |
| 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 76 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| Claude Opus 5 | Claude Opus 5.5 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 常駐開啟 |
| 結構化輸出 | 是 | 是 |
| 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 5 | Claude Opus 5.5 | |
|---|---|---|
| 輸入模態 | 文字 影像 | 文字 影像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-07-24 | 2026-09-22 |
| 知識截止日期 | 2026-05 | 2026-06 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 128K |
| 思考參數 |
| output_config.effort |
| 可接受的值 | thinking.type
effort
| output_config.effort
|
| 預設值 | thinking on effort
| medium |
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 5 · Claude Opus 5.5
單一提示詞，兩款模型 — 經由閘道測量
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
Claude Opus 5.5 通過 · 3 sentences
When we turned on reasoning, the model began "thinking out loud" before each answer, generating thousands of internal working tokens that users never see but that we are billed for just like the final response. Those hidden tokens are charged at the output rate, typically the most expensive rate, so the same question with the same visible answer now costs roughly twice as much or more. We can bring the bill back down without losing the quality gains by capping the reasoning budget and reserving reasoning mode for the complex tasks that actually benefit from it.
輸出 263 tok (+102 思考) 延遲 5.7 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。
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
Claude Opus 5.5 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 ``` **Explanation:** The original code counted *matching adjacent pairs* rather than *items in a run*. Both `cur` and `best` started at 0, and `cur` reset to 0 on a mismatch, so a run of length *n* was reported as *n − 1*. For a single-element list the loop never executes, so it returned 0 instead of 1. For `[5, 5]` it returned 1 instead of 2. The fix treats every element as the start of a run of length 1: `best` and `cur` start at 1, and `cur` resets to 1 (not 0) when the value changes. An explicit check returns 0 for an empty list. The "misses runs at the last index" reports are the same off-by-one bug rather than a separate one. […]
輸出 723 tok (+303 思考) 延遲 10.4 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。
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
Claude Opus 5.5 通過 · 5/5 fields, guidance "Withheld pending Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` **Judgment calls to check:** - **quarter**: Kept as "September quarter" rather than "Q3", since the fiscal calendar isn't stated. The text mentions an upcoming "Q4 close," so it may be fiscal Q3, but that's not certain. - **revenue_usd_m**: The text says only "412 for the topline." It doesn't state currency or units, and the figure is on a constant-currency basis, not reported USD. USD millions is an assumption. Use `null` if you need strictly stated values. - **growth_pct**: The source says "eleven and change," so the true figure is slightly above 11. 11 is an approximation. - **guidance**: Guidance was explicitly withheld. I recorded that rather than `null`, since the text addresses it directly.
輸出 829 tok (+488 思考) 延遲 10.9 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。
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
Claude Opus 5.5 通過 · 120 words, 0 banned, 1 question
Today we're launching Cross-Provider Prompt Caching for our API gateway. Why pay twice for the same context? Our gateway now stores repeated prompt prefixes, system instructions, and document chunks, then reuses them whether your request goes to OpenAI, Anthropic, Google, or an open-source model. Teams running retrieval pipelines or long agent loops can cut token costs by up to 60 percent and trim latency on every cached call. Setup takes one configuration flag; no code changes are required. Cache rules respect tenant boundaries, expire on your schedule, and log every hit for audit review. Dashboards show savings by provider, route, and application in real time. Cross-Provider Prompt Caching is available on all Pro and Enterprise plans. Turn it on today.
輸出 1263 tok (+1007 思考) 延遲 13.5 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-5",
# model="claude-opus-5-5", # 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)import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://synthorai.io/v1",
apiKey: "sk-syn-...",
});
const resp = await client.chat.completions.create({
model: "claude-opus-5",
// model: "claude-opus-5-5", // 取消註解此行，並註解上一行
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);curl https://synthorai.io/v1/chat/completions \
-H "Authorization: Bearer sk-syn-..." \
-H "Content-Type: application/json" \
-d '{
"model": "claude-opus-5",
# "model": "claude-opus-5-5", # 取消註解此行，並註解上一行
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'package main
import (
"context"
"fmt"
"github.com/openai/openai-go/v3"
"github.com/openai/openai-go/v3/option"
)
func main() {
client := openai.NewClient(
option.WithBaseURL("https://synthorai.io/v1"),
option.WithAPIKey("sk-syn-..."),
)
resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
Model: "claude-opus-5",
// Model: "claude-opus-5-5", // 取消註解此行，並註解上一行
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("claude-opus-5")
// .model("claude-opus-5-5") // 取消註解此行，並註解上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Claude Opus 5 和 Claude Opus 5.5 哪個比較便宜？
Claude Opus 5.5 在 輸入 / 1m tokens 上較便宜（$4 對比 $5，相差 1.3×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。
我可以在不進行兩次整合的情況下，對 Claude Opus 5 和 Claude Opus 5.5 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 5 與 Claude Opus 5.5 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。