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Claude Fable 5 vs Claude Fable 5.1

Claude Fable 5 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。

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。

vs

什麼情況選哪一個

這兩者在事實上幾乎相同:皆接受文字與影像輸入、皆回傳文字、皆具備 1000000-token 的上下文與 128000 的最大輸出,相同的聊天、程式碼、思考、工具與推理旗標,無法關閉的思考功能,以及同樣的每百萬 token 輸入 $10 與輸出 $50。區別在於快取讀取與最新程度:claude-fable-5-1 每百萬快取輸入 token 收費 $0.25,而 claude-fable-5 為 $1,差距達 4x,並且知識截止日期為 2026-06,對比 2026-01。對於重度依賴快取或重複前綴的工作負載,請選擇 5.1;只有在您已經指定使用它的情況下,才繼續使用 claude-fable-5。

Benchmark 成績

領先高於平均沒有模型更高Claude Fable 5686 / 9740 / 97Claude Fable 5.11219 / 213 / 21

兩邊都有成績的有 18 項。

Claude Fable 5 Claude Fable 5.1 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
69.7%
67.4%
BioMysteryBench hard
46.5%
N/A
OSWorld 2.0 partial
72.9%
80.7%
Cybergym
83.1%
N/A
HealthBench Professional
60.9%
58.1%
Terminal-Bench-Science 0.1
24.7%
52.6%
Harvey Lab-AA
93.6%
N/A
GPQA Diamond
92.6%
93.7%
Blueprint-Bench 2
38.6%
N/A
AutomationBench
17.1%
31.4%
CharXiv (RQ) reasoning
沒有模型分數更高 88.9%
N/A

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

定價

Claude Fable 5 Claude Fable 5.1 Δ
輸入 / 1M tokens $10 $10 =
輸出 / 1M tokens $50 $50 =
快取讀取 / 1M tokens $1 $0.25 4×
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 79 個聊天模型(對數尺度)

功能

Claude Fable 5 Claude Fable 5.1
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 Claude Fable 5.1
輸入模態 文字 圖像 文字 圖像
輸出模態 文字 文字
發布日期 2026-06-09 2026-09-01
知識截止日期 2026-01 2026-06
上下文視窗 1M 1M
最大輸出 128K 128K
思考參數 output_config.effort (thinking.type is adaptive-only and needs no configuration) output_config.effort (thinking is adaptive-only and always on)
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max

both "enabled" and "disabled" return 400

effort
  • low
  • medium
  • high
  • xhigh
  • max
預設值

thinking always on (adaptive)

effort
  • high

thinking always on (adaptive)

effort
  • high

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 · Claude Fable 5.1

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 Fable 5 通過 · 3 sentences

When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.

輸出 228 tok 延遲 6.7 s

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

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 Fable 5 通過 · 8/8 cases

## Fixed code ```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 ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when

輸出 531 tok 延遲 12.5 s

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

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 Fable 5 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 interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.

輸出 192 tok 延遲 6.5 s

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

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 Fable 5 通過 · 120 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.

輸出 1173 tok 延遲 18.2 s

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

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",
    # model="claude-fable-5-1",  # 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 Fable 5 和 Claude Fable 5.1 哪個比較便宜?

兩者的「輸入 / 1M tokens」相同($10),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 Fable 5 和 Claude Fable 5.1 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 與 Claude Fable 5.1 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