Claude Fable 5.1 vs Claude Opus 5.5
Claude Fable 5.1 採邀請制。下方的數字是即時費率，但要先取得工作空間的授權才能呼叫；如果你打算依據這份比較來開發，請先向我們申請開通。
什麼情況選哪一個
這兩者是 Claude 系列的相鄰等級：claude-fable-5-1 是最高等級，Anthropic 將其定位於高要求推理與長程代理工作，而 claude-opus-5-5 是其次一等級，專為長時間運行的代理程式設計與知識工作而打造。兩者皆接受文字與影像輸入，具備 1000000-token 上下文與 128000 最大輸出，並始終開啟思考功能，因此價格標示了此階差：claude-fable-5-1 的成本為輸入 $10 與輸出 $50，對比於 $4 與 $20，兩者皆高出 2.5x，外加快取讀取的 $0.25 對比 $0.2。請預設使用 claude-opus-5-5，並當您自身的評估結果顯示值得支付升級費用時，再將最困難的長程任務轉移至 claude-fable-5-1。
Benchmark 成績
兩邊都有成績的有 9 項。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Claude Fable 5.1 | Claude Opus 5.5 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $10 | $4 | 2.5× |
| 輸出 / 1M tokens | $50 | $20 | 2.5× |
| 快取讀取 / 1M tokens | $0.25 | $0.2 | 1.3× |
| 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 77 個聊天模型（對數尺度）
功能
| Claude Fable 5.1 | Claude Opus 5.5 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 一律開啟 | 一律開啟 |
| 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 Fable 5.1 | Claude Opus 5.5 | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 圖像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-01 | 2026-09-22 |
| 知識截止日期 | 2026-06 | 2026-06 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 128K |
| 思考參數 | output_config.effort (thinking is adaptive-only and always on) | output_config.effort |
| 可接受的值 | effort
| output_config.effort
|
| 預設值 | thinking always on (adaptive) effort
| 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 Fable 5.1 · Claude Opus 5.5
同一段提示詞，兩個模型，經閘道實測
Claude Fable 5.1 通過 · 3 sentences
When we enabled reasoning, the model started generating a long chain of internal "thinking" tokens before producing each answer, and we're billed for those tokens at the same output rate even though users never see them. In practice, that hidden reasoning often runs several times longer than the visible response, so the total tokens per request roughly doubled while the number of requests stayed flat. The upside is measurably better accuracy on complex tasks, so the right conversation is whether that quality gain justifies the cost—and if not, we can restrict reasoning to the workflows that actually need it rather than turning it on globally.
輸出 186 tok 延遲 3.2 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
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。
Claude Fable 5.1 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)): cur = cur + 1 if xs[i] == xs[i - 1] else 1 best = max(best, cur) return best ``` The bug is that `cur` counts *matches between adjacent elements* rather than *elements in the run*: it starts at 0 and only increments when `xs[i] == xs[i-1]`, so a run of length k produces a count of k−1. That's why a single element returns 0 instead of 1, and why every other run also comes out one short — the "misses runs at the end" reports are the same off-by-one seen on inputs like `[1, 2, 2]`, where the trailing run of length 2 is reported as 1 (the original code does update `best` inside the loop, so end-of-list runs aren't actually skipped, just undercounted). […]
輸出 882 tok (+507 思考) 延遲 3.4 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。
Claude Fable 5.1 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on judgment calls (not part of the object): - **revenue_usd_m**: The text says "412 for the topline" but never states the currency or that the figure is in millions; it's also on a constant-currency basis and prefaced with "call it," so it's approximate. If you want strict literalism, this should be `null`. - **growth_pct**: "Eleven and change" means slightly above 11%; the exact figure isn't given. - **quarter**: "September quarter" is Q3 on a calendar basis, consistent with the reference to a pending Q4 close. - **guidance**: Explicitly withheld, so `null`.
輸出 633 tok (+349 思考) 延遲 3.8 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
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。
Claude Fable 5.1 通過 · 121 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching for the Gateway.** Starting today, your API gateway can cache prompts once and reuse them across OpenAI, Anthropic, Google, and any other provider you route to. Long system instructions, retrieval context, and few-shot examples are stored at the gateway layer, so switching models no longer means paying to resend the same tokens. Why send the same 8,000-token preamble to three different vendors when you could send it once? Our cache normalizes prompt segments, fingerprints them, and serves matching prefixes from memory before the request ever reaches an upstream API. Early customers report token costs falling by 30 to 60 percent on high-volume workloads, with median latency dropping alongside. Available today on all Team and Enterprise plans.
輸出 1722 tok (+1463 思考) 延遲 2.9 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
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-fable-5-1",
# model="claude-opus-5-5", # 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)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-fable-5-1",
// model: "claude-opus-5-5", // 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",
});
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-fable-5-1",
# "model": "claude-opus-5-5", # 取消這一行的註解，並把上一行註解掉
"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-fable-5-1",
// Model: "claude-opus-5-5", // 取消這一行的註解，並把上一行註解掉
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-fable-5-1")
// .model("claude-opus-5-5") // 取消這一行的註解，並把上一行註解掉
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Claude Fable 5.1 和 Claude Opus 5.5 哪個比較便宜？
以「輸入 / 1M tokens」來看，Claude Opus 5.5 比較便宜（$4 對 $10，相差 2.5×）。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 Fable 5.1 和 Claude Opus 5.5 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 Fable 5.1 與 Claude Opus 5.5 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。