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Claude Fable 5 vs Gemini 3.7 Flash

Claude Fable 5 已從我們的目錄下架。下方是它最後公布的費率;它已經無法呼叫,這組比較裡的另一個模型則仍可呼叫。

vs

什麼情況選哪一個

claude-fable-5 在所有可比較的項目上都比 gemini-3.7-flash 貴約 13.3x:每百萬輸入 token 為 $10 對 $0.75,輸出為 $50 對 $3.75,快取讀取為 $1 對 $0.075。為了無法關閉的常駐思考功能以及更大的回覆空間(高達 128000 輸出 token,對比 65536)請選擇它。針對高用量工作,或是當你需要音訊或影片輸入時請選擇 gemini-3.7-flash,它具備相當的 1048576-token 上下文,對比於 1000000。

Benchmark 成績

領先高於平均沒有模型更高Claude Fable 5986 / 9740 / 97Gemini 3.7 Flash317 / 233 / 23

兩邊都有成績的有 12 項。

Claude Fable 5 Gemini 3.7 Flash 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%
65.3%
BioMysteryBench hard
46.5%
43.5%
OSWorld 2.0
66.1%
47.9%
Cybergym
83.1%
N/A
HealthBench Professional
60.9%
N/A
Finance Agent v2
56.3%
59%
Harvey Lab-AA
93.6%
90.7%
GPQA Diamond
92.6%
N/A
Blueprint-Bench 2
38.6%
N/A
AutomationBench (v1.0.6)
46.2%
52.3%
LVBench
N/A
沒有模型分數更高 85.4%

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

定價

Claude Fable 5 Gemini 3.7 Flash Δ
輸入 / 1M tokens $10 $0.75 13×
輸出 / 1M tokens $50 $3.75 13×
快取讀取 / 1M tokens $1 $0.075 13×
快取寫入 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 76 個聊天模型(對數尺度)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

Claude Fable 5 Gemini 3.7 Flash
工具使用 是 是
思考控制 一律開啟 是,但供應商未公布調整參數
結構化輸出 是 是
提示詞快取 顯式(由你標記前綴) 隱式 + 顯式
快取存活時間 5m default, 1h option 未公布
最小快取前綴 1024 個 token 4096 個 token

規格

Claude Fable 5 Gemini 3.7 Flash
輸入模態 文字 圖像 文字 圖像 音訊 影片
輸出模態 文字 文字
發布日期 2026-06-09 2026-08-13
知識截止日期 2026-01 2026-03
上下文視窗 1M 1M
最大輸出 128K 66K
思考參數 output_config.effort (thinking.type is adaptive-only and needs no configuration) -
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max

both "enabled" and "disabled" return 400

-
預設值

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 · Gemini 3.7 Flash

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

Gemini 3.7 Flash 通過 · 3 sentences

Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.

輸出 878 tok (+799 思考) 延遲 6.9 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

Gemini 3.7 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 else: cur = 1 best = max(best, cur) return best ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.

輸出 1050 tok (+827 思考) 延遲 6.4 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

Gemini 3.7 Flash 通過 · 5/5 fields, guidance "withheld"

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

輸出 918 tok (+858 思考) 延遲 6.2 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

Gemini 3.7 Flash 通過 · 120 words, 0 banned, 1 question

Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.

輸出 2858 tok (+2718 思考) 延遲 14.1 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-fable-5",
    # model="gemini-3.7-flash",  # 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 Gemini 3.7 Flash 哪個比較便宜?

以「輸入 / 1M tokens」來看,Gemini 3.7 Flash 比較便宜($0.75 對 $10,相差 13×)。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 和 Gemini 3.7 Flash 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 Gemini 3.7 Flash 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