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Claude Fable 5.1 vs Kimi K2.7 Code

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

當工作需要大空間時,請選擇 claude-fable-5-1:它的 1000000-token 上下文幾乎是 kimi-k2.7-code 的 4x,其 128000-token 最大輸出約大 4x,並且在聊天、程式碼、工具和推理之外帶有明確的思考旗標。當成本或影片輸入是決策關鍵時,請選擇 kimi-k2.7-code,因為它接受文字、影像與影片,且收費為輸入 $0.95 及輸出 $4,對比 $10 及 $50 - 大約便宜 10.5x 與 12.5x - 快取讀取則為 $0.19 對比 $0.25。兩者皆為回傳文字且具備工具使用能力的模型,其思考功能皆無法被關閉。

Benchmark 成績

高於平均沒有模型更高Claude Fable 5.119 / 213 / 21Kimi K2.7 Code1 / 50 / 5
Claude Fable 5.1 Kimi K2.7 Code 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
67.4%
N/A
OSWorld 2.0 partial
80.7%
N/A
HealthBench Professional
58.1%
N/A
Terminal-Bench-Science 0.1
52.6%
N/A
GPQA Diamond
93.7%
N/A
MCP-Atlas
N/A
76%
Chartography with tools
88.4%
N/A

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

定價

Claude Fable 5.1 Kimi K2.7 Code Δ
輸入 / 1M tokens $10 $0.95 11×
輸出 / 1M tokens $50 $4 13×
快取讀取 / 1M tokens $0.25 $0.19 1.3×
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 79 個聊天模型(對數尺度)

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

功能

Claude Fable 5.1 Kimi K2.7 Code
工具使用 是 是
思考控制 一律開啟 一律開啟
結構化輸出 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 5.1 Kimi K2.7 Code
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 2026-09-01 2026-06
知識截止日期 2026-06 -
上下文視窗 1M 256K
最大輸出 128K 33K
思考參數 output_config.effort (thinking is adaptive-only and always on)
  • thinking.type
  • thinking.keep
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max
type
  • accepts only enabled
keep
  • accepts only all
預設值

thinking always on (adaptive)

effort
  • high
thinking on with Preserved Thinking on

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 · Kimi K2.7 Code

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 5.1 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

Kimi K2.7 Code 通過 · 3 sentences

Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.

輸出 259 tok (+174 思考) 延遲 5.4 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 5.1 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

Kimi K2.7 Code 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 that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.

輸出 410 tok (+222 思考) 延遲 9.4 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 5.1 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

Kimi K2.7 Code 通過 · 5/5 fields, guidance "withheld pending the Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```

輸出 721 tok (+663 思考) 延遲 13.2 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 5.1 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

Kimi K2.7 Code 通過 · 120 words, 0 banned, 1 question

We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.

輸出 2375 tok (+2235 思考) 延遲 38.6 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="kimi-k2.7-code",  # 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 5.1 和 Kimi K2.7 Code 哪個比較便宜?

以「輸入 / 1M tokens」來看,Kimi K2.7 Code 比較便宜($0.95 對 $10,相差 11×)。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 Kimi K2.7 Code 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 Kimi K2.7 Code 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