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Claude Fable 5.1 vs Gemini 3.8 Flash

Claude Fable 5.1 採邀請制提供服務。下方的數據為即時費率,但進行呼叫前需先取得工作區授權;在您基於此比較進行開發之前,請先向我們申請存取權限。

vs

何時用哪一個

兩者發布時間僅相差一天,皆在約一百萬個 token 的上下文中接受文字與圖片輸入,但費率表卻有顯著差異:claude-fable-5-1 每個 token 的輸入($10 對比 $0.75)與輸出($50 對比 $3.75)大約貴了 13x,在快取讀取上則大約貴了 3x($0.25 對比 $0.075)。當您需要其無法停用的常駐思考功能,或是在單次回覆中需要高達 128000 個輸出 token 時,請選擇 claude-fable-5-1。對於高用量或對價格敏感的工作,或者當您需要音訊與視訊輸入,且可接受其 65536 最大輸出限制時,請選擇 gemini-3.8-flash。

Benchmark 成績

領先高於同儕均值無人分數更高Claude Fable 5.1416 / 185 / 18Gemini 3.8 Flash212 / 165 / 16

雙方都被測過的 6 項。

Claude Fable 5.1 Gemini 3.8 Flash 其他被測模型 同儕均值 無人分數更高
DeepSWE 1.1
67.4%
73.7%
BioMysteryBench hard
N/A
無人分數更高 56.5%
OSWorld 2.0 Partial score, batch tool enabled
N/A
59%
HealthBench Professional
58.1%
52.1%
Finance Agent v2
N/A
無人分數更高 61.4%
Legal Agent Benchmark
N/A
10%
GPQA Diamond
93.7%
95.3%
AutomationBench
31.4%
N/A
CharXiv (RQ) no tools
N/A
無人分數更高 86.2%

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

定價

Claude Fable 5.1 Gemini 3.8 Flash Δ
輸入 / 1M tokens $10 $0.75 13×
輸出 / 1M tokens $50 $3.75 13×
快取讀取 / 1M tokens $0.25 $0.075 3.3×
快取寫入 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 71 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

能力

Claude Fable 5.1 Gemini 3.8 Flash
工具使用
思考控制 常駐開啟 是 —— 廠商未公布調節參數
結構化輸出
提示快取 顯式(由您標記前綴) 隱式 + 顯式
快取生命週期 5m default, 1h option 未公開
最小快取前綴 1024 個 token 4096 個 token

規格

Claude Fable 5.1 Gemini 3.8 Flash
輸入模態 文字 影像 文字 影像 音訊 影片
輸出模態 文字 文字
發布日期 2026-09-01 2026-09-02
知識截止日期 2026-06 2026-03
上下文視窗 1M 1M
最大輸出 128K 66K
思考參數 output_config.effort (thinking is adaptive-only and always on) -
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
預設值

thinking always on (adaptive)

effort
  • high
-

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 Fable 5.1 · Gemini 3.8 Flash

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 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

Gemini 3.8 Flash 通過 · 3 sentences

When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.

輸出 705 tok (+624 思考) 延遲 6.4 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 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

Gemini 3.8 Flash 通過 · 8/8 cases

Here is the corrected 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 best = max(best, cur) else: cur = 1 return best ``` ### Explanation of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.

輸出 1393 tok (+1149 思考) 延遲 10.1 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 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

Gemini 3.8 Flash 通過 · 5/5 fields, guidance null

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

輸出 3989 tok (+3932 思考) 延遲 30.4 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 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

Gemini 3.8 Flash 通過 · 120 words, 0 banned, 1 question

Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.

輸出 3833 tok (+3688 思考) 延遲 21.8 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-fable-5-1",
    # model="gemini-3.8-flash",  # 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 Fable 5.1 和 Gemini 3.8 Flash 哪個比較便宜?

Gemini 3.8 Flash 在 輸入 / 1m tokens 上較便宜($0.75 對比 $10,相差 13×)。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 Fable 5.1 和 Gemini 3.8 Flash 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 Fable 5.1 與 Gemini 3.8 Flash 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