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Claude Fable 5 vs Kimi K3

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

kimi-k3 在整個費率表上比 claude-fable-5 便宜約 3.3x(每百萬輸入 $3 對 $10,輸出 $15 對 $50,快取讀取 $0.3 對 $1),並且它也接受影片輸入,能在其 1,048,576-token 的視窗內生成最高 1,048,576 輸出 token。claude-fable-5 提供 1,000,000-token 的上下文但將輸出限制在 128,000 token,且它具備明確的思考能力以及 January 2026 的知識截止日期。針對影片輸入與較低成本的極長生成請選擇 kimi-k3;當該思考能力與所述的資料新舊程度比價格更重要時請選擇 claude-fable-5。

Benchmark 成績

領先高於平均沒有模型更高Claude Fable 54087 / 9740 / 97Kimi K31752 / 6712 / 67

兩邊都有成績的有 58 項,其中 1 項平手。

Claude Fable 5 Kimi K3 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%
67.5%
BioMysteryBench hard
46.5%
N/A
OSWorld-Verified
沒有模型分數更高 85%
84.8%
Cybergym
83.1%
80%
HealthBench Professional
60.9%
N/A
Finance Agent v2
56.3%
54.4%
Harvey Lab-AA
93.6%
沒有模型分數更高 94.6%
GPQA Diamond
92.6%
93.5%
Blueprint-Bench 2
38.6%
N/A
BrowseComp
87.4%
91.2%
CharXiv (RQ) reasoning
沒有模型分數更高 88.9%
84.8%

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

定價

Claude Fable 5 Kimi K3 Δ
輸入 / 1M tokens $10 $3 3.3×
輸出 / 1M tokens $50 $15 3.3×
快取讀取 / 1M tokens $1 $0.3 3.3×
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)

Claude Fable 5 · $10 Kimi K3 · $3
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

功能

Claude Fable 5 Kimi K3
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) 隱式(自動)
快取存活時間 5m default, 1h option 未公布
最小快取前綴 1024 個 token 未公布

規格

Claude Fable 5 Kimi K3
輸入模態 文字 圖像 文字 圖像 影片
輸出模態 文字 文字
發布日期 2026-06-09 2026-07-16
知識截止日期 2026-01 -
上下文視窗 1M 1M
最大輸出 128K 1M
思考參數 output_config.effort (thinking.type is adaptive-only and needs no configuration) reasoning_effort (top-level; the thinking object is not accepted)
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max

both "enabled" and "disabled" return 400

reasoning_effort
  • low
  • high
  • max
預設值

thinking always on (adaptive)

effort
  • high
max

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 · Kimi K3

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

Kimi K3 通過 · 3 sentences

Reasoning models don't just answer questions—they "think" first, generating long internal chains of step-by-step logic before producing a response. Those hidden thinking steps are billed as output tokens (the most expensive kind, typically 3–5x the price of input tokens), and a single query can generate thousands of them even when the visible answer is only a paragraph long. So you're paying for dramatically more compute per request: the bill doubled because the model does far more work behind the scenes, not because usage increased.

輸出 755 tok (+637 思考) 延遲 20.8 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

Kimi K3 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 ``` The bug is a fencepost error: `cur` counts *matching adjacent pairs* rather than *items in the run*, because it starts at 0 and only increments when `xs[i] == xs[i - 1]`. A run of length L contains L−1 equal pairs, so every run was undercounted by exactly one. For a single-element list (a run of length 1) the loop never runs and the function returns 0 instead of 1; likewise any run ending at the last index — e.g. the two `2`s in `[1, 2, 2]` — reported 1 instead of 2, making it look like no run existed at all. […]

輸出 1837 tok (+1547 思考) 延遲 47.2 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

Kimi K3 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

輸出 924 tok (+863 思考) 延遲 25.9 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。

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

Kimi K3 通過 · 120 words, 0 banned, 1 question

Meet Prompt Cache, a new API gateway capability that stores prompt responses and serves them across OpenAI, Anthropic, Google, and Azure endpoints. It matches requests by model, prompt hash, tools, temperature, and tenant policy, so repeated work returns fast while sensitive contexts stay isolated. Teams set TTLs, stale rules, encryption scopes, and bypass flags per route. Analytics show hit rate, latency saved, token spend avoided, and drift risk by provider. What changes for developers? Keep one integration, add cache headers, and watch fallback logic respect consent, residency, and audit needs. During rollout, canary keys compare fresh answers with cached copies before promotion. Prompt Cache cuts vendor calls, steadies p95 latency, and gives platform owners controls for cost, quality, and compliance.

輸出 1527 tok (+1354 思考) 延遲 37.9 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="kimi-k3",  # 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)

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常見問題

Claude Fable 5 和 Kimi K3 哪個比較便宜?

以「輸入 / 1M tokens」來看,Kimi K3 比較便宜($3 對 $10,相差 3.3×)。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 Kimi K3 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 Kimi K3 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