新用戶 免費註冊,送 10 次呼叫,最高 $1,免綁卡。

Claude Fable 5.1 vs Qwen3.8 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

兩者皆具備 1,000,000-token 上下文並接受文字與圖片,但價目表截然不同:claude-fable-5-1 收取每百萬 token $10 的輸入與 $50 的輸出,大約是 qwen3.8-flash $0.15 與 $0.47 的 67x 與 106x。當您希望在對話、程式碼與工具使用中獲得其常啟思考功能(無法停用)時,請選擇 claude-fable-5-1;針對成本至關重要的高處理量工作、需要影片輸入、稍大的 131,072-token 最大輸出,或需要依據每個請求關閉思考功能的能力時,請選擇 qwen3.8-flash。

Benchmark 成績

高於同儕均值無人分數更高Claude Fable 5.116 / 185 / 18Qwen3.8 Flash13 / 163 / 16
Claude Fable 5.1 Qwen3.8 Flash 其他被測模型 同儕均值 無人分數更高
DeepSWE 1.1
67.4%
58.7%
OSWorld 2.0 partial
無人分數更高 77.9%
52.3%
HealthBench Professional
58.1%
N/A
JobBench
N/A
55.7%
GPQA Diamond
93.7%
91.7%
ERQA
N/A
無人分數更高 72.3%
AutomationBench
31.4%
N/A
LVBench
N/A
76.6%

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

定價

Claude Fable 5.1 Qwen3.8 Flash Δ
輸入 / 1M tokens $10 $0.15 67×
輸出 / 1M tokens $50 $0.47 106×
快取讀取 / 1M tokens $0.25 $0.016 16×
快取寫入 1.25x (5m) / 2x (1h) 1.25x -

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)

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

能力

Claude Fable 5.1 Qwen3.8 Flash
工具使用
思考控制 常駐開啟 可配置
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 explicit: 5m, reset on hit
最小快取前綴 1024 個 token 1024 個 token

規格

Claude Fable 5.1 Qwen3.8 Flash
輸入模態 文字 影像 文字 影像 影片
輸出模態 文字 文字
發布日期 2026-09-01 2026-08-27
知識截止日期 2026-06 -
上下文視窗 1M 1M
最大輸出 128K 131K
思考參數 output_config.effort (thinking is adaptive-only and always on)
  • enable_thinking
  • thinking_budget
  • preserve_thinking
可接受的值
effort
  • low
  • medium
  • high
  • xhigh
  • max
enable_thinking
  • true
  • false
thinking_budget
  • in tokens
preserve_thinking
  • true
  • false
預設值

thinking always on (adaptive)

effort
  • high

on

qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off

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 · Qwen3.8 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

Qwen3.8 Flash 通過 · 3 sentences

Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.

輸出 330 tok (+262 思考) 延遲 8.9 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

Qwen3.8 Flash 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.

輸出 2123 tok (+1933 思考) 延遲 43.8 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

Qwen3.8 Flash 通過 · 5/5 fields, guidance null

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

輸出 519 tok (+463 思考) 延遲 10.5 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

Qwen3.8 Flash 通過 · 120 words, 0 banned, 1 question

We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.

輸出 5958 tok (+5805 思考) 延遲 88.8 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="qwen3.8-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 Qwen3.8 Flash 哪個比較便宜?

Qwen3.8 Flash 在 輸入 / 1m tokens 上較便宜($0.15 對比 $10,相差 67×)。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 Qwen3.8 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 Qwen3.8 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